Backtest & Code Momentum Strategies

@sdqtrades

With my extensive background in AI, automation, and algorithmic trading strategies, I'm confident in my ability to expertly handle your project. In terms of the backtest phase, I will rely on Python's efficient data analysis libraries such as Pandas and Numpy to run comprehensive backtests on all the momentum-based stock trading strategies you provide. This will include calculating key performance metrics, drawdowns, trade statistics, and producing easy-to-understand equity curves for you. When it comes to coding the final scripts for live trading, I am proficient in Pine Script as well as Python. I'd suggest we use Python as it offers more flexibility and broader market integration possibilities. Rest assured that the code delivered will faithfully reproduce the tested results when given the exact same input data. Moreover, I'll ensure your script is well-commented for easier parameter tweaking in the future. The accompanying read-me file will also outline a straightforward guide for installation and usage. Keeping your project's confidentiality and intellectual property rights is paramount to me. You can trust that your ideas remain 100% confidential and you'll have full ownership of both software and source code at the completion of this project.

@HarjitHundal91

Work can be done Anything in the ***PineScript*** or ***Python*** can be coded just need a good description of the concept Bid Price and Duration to Complete the Project Will be Revised after getting Full Description of the Project Rest we can discuss in chat

@vishal1145

Your equity curves will be meaningless if the backtest engine doesn't account for slippage and partial fills during high-volatility periods. Most momentum strategies look profitable in simulation but fail live because the backtest assumes instant execution at mid-price. Before I architect the testing framework, I need clarity on two things. First, what's your expected holding period - are we talking intraday mean reversion or multi-week trend following? This determines whether I build around tick data or daily bars. Second, do you have a broker API in mind for live execution, or are we just generating signals? If it's Interactive Brokers or Alpaca, I'll structure the code to match their order routing logic so you're not rewriting everything during deployment. Here's the architectural approach: - PYTHON + BACKTRADER: Build a vectorized backtest engine with realistic commission models and configurable slippage assumptions to stress-test each strategy under adverse conditions. - STATISTICS + MONTE CARLO: Run 1000+ simulations with randomized entry timing to calculate confidence intervals on your Sharpe ratio and max drawdown, not just point estimates. - DATA VISUALIZATION + PLOTLY: Generate interactive equity curves, underwater plots, and rolling metrics so you can spot regime changes where the strategy breaks down. - PINE SCRIPT TRANSLATION: Once the logic is validated, I'll port it to TradingView with alert webhooks if you want visual confirmation before going fully automated. - SOFTWARE ARCHITECTURE: Structure the codebase with separate modules for data ingestion, signal generation, position sizing, and risk management so you can swap out components without breaking the system. I've built 8 systematic trading strategies for prop shops and family offices, including a momentum rotation system that managed $4M and survived the 2022 drawdown with a 15% max DD. The backtest phase takes 3-4 days to ensure statistical rigor. Production code delivery is another 2-3 days after you approve the metrics. I don't take on projects where the data quality is questionable or the performance targets are unrealistic. Let's schedule a 20-minute call to walk through your historical data structure and discuss whether your momentum signals have enough edge after transaction costs.

@mayankinnovative

Hi, As per my understanding: You already have a Wix website generating good organic traffic, and your goal is to modernize the design without affecting existing SEO performance. The focus is on improving layout structure, typography, responsiveness, and user experience while maintaining the current color palette and lead generation capability. Implementation approach: I will redesign your existing Wix website with a clean, modern structure and refined typography that improves readability and conversion flow across desktop and mobile devices. The development will remain fully within Wix as required. Alongside the visual upgrade, I will perform on-page SEO optimization, speed improvements, metadata checks, image optimization, and Search Console/Analytics monitoring to maintain or improve rankings after launch. I can also support long-term SEO under a monthly retainer with regular reporting and lead tracking improvements. A few quick questions: 1. How many pages are currently on the Wix website? 2. Do you already have preferred typography/style references? 3. Which lead tracking tools or forms are currently connected to the site?

@Shrutibimpl

Hello There, As per my understanding you want a validated backtesting framework and a production ready automation script for your momentum based trading strategies. 1) Do these momentum strategies rely on intra day price action or daily close signals to execute trades? 2) Should the automation script integrate with a specific brokerage API like Interactive Brokers, Zerodha, or Alpaca for live execution? 3) Are there specific risk management rules such as trailing stops or portfolio rebalancing that must be included in the backtest logic? I will provide you with a clear picture of how your strategies perform in real world conditions so you can trade with absolute certainty before risking any capital. You will get a professional dashboard showing your profit potential and risk levels, followed by a reliable automation tool that handles the execution for you. This means you can step away from the charts and let the code manage your portfolio, knowing every trade follows your proven rules without hesitation or manual error. I will build the backtesting engine using VectorBT or Pandas for high performance vectorized calculations to ensure rapid iteration across your historical data. The automation script will be developed in Python, utilizing a modular structure where strategy logic is separated from API connectivity for easy maintenance and future updates. Best regards, Bharat Joshi

@prashanpatel557

Hello, I can help you rigorously backtest your momentum-based stock strategies and then convert the validated logic into clean, production-ready code. Phase 1 – Backtesting & Analysis • Import your historical price data and precomputed indicators • Implement each strategy exactly as specified • Generate detailed reports including: * Net P&L, CAGR, Sharpe Ratio * Max Drawdown * Win Rate and Expectancy * Trade-by-trade logs * Equity curve charts Phase 2 – Production Code • Convert approved strategies into Python or Pine Script • Ensure results match the backtest • Write clean, well-commented, modular code • Provide setup guide and usage documentation Preferred Toolset • Python + VectorBT / Backtrader / Pandas • Optional Pine Script for TradingView alerts and automation Deliverables • Backtest reports and charts • Source code • README and parameter guide Timeline: 5–10 days depending on the number of strategies Budget: ₹25,000 – ₹35,000 I have extensive experience in quantitative research, backtesting, and automated trading system development. Please share how many strategies you want to evaluate and your preferred deployment platform.

@KarthikTechCon

Hello, I’m Karthik with 15+ years of experience in quantitative trading systems, Python automation, financial analytics, and backtesting frameworks. I can help backtest your momentum-based trading strategies, optimize the logic where required, and convert the validated setups into clean, production-ready automated scripts. My approach includes: • Strategy validation using your historical price and indicator datasets • Backtesting with detailed metrics: * CAGR, Sharpe ratio, win rate, drawdown * Trade statistics and exposure analysis * Equity curves and performance comparison • Parameter tuning and iterative optimization • Production-grade implementation with reproducible results • Cleanly commented code and setup documentation Suggested Toolset: • Python with Pandas, NumPy, Backtrader/vectorbt/Zipline • Plotly/Matplotlib for visual analytics • Pine Script if TradingView deployment is preferred Deliverables: • Backtest reports with interpretable charts • Optimized strategy scripts • ReadMe with installation, configuration, and usage • Reproducible outputs using your supplied datasets Estimated Timeline: • Backtesting & analysis: 3–5 days • Optimization & revisions: 2–4 days • Final automation scripts & documentation: 2–3 days I focus on accurate modeling, maintainable code, and realistic strategy validation. Regards, Karthik 15+ Years Experience

@kingxanda

Hey, Thanks for your post. I'v read your description carefully. I have relevant experience I can help. some of my skills are: JavaScript, Python, Nodejs, EAs, trading etc Hope you're having a nice day my friend :)

@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

@kshubham102

Hi, I’d be happy to help with both the strategy validation and the automation side of this project. Since you already have historical price data and indicator libraries prepared, we can move directly into structured backtesting and optimization. My approach would be: • Build a clean backtesting pipeline in Python using pandas, NumPy and frameworks like Backtrader/vectorbt depending on strategy complexity • Evaluate each momentum strategy with detailed metrics including: CAGR / total return Sharpe & Sortino ratios Max drawdown Win rate & trade statistics Equity curves and position analysis After validation, I’ll convert the finalized strategies into production-ready code (Python or Pine Script if TradingView compatibility is preferred). The code will: • reproduce the same backtest results • be modular and well-commented • allow easy parameter adjustments • include setup instructions and usage examples Deliverables: • Full backtest reports + visual equity curves • Strategy optimization suggestions • Production-ready trading scripts • README/documentation Toolset: • Backtesting: Python + Backtrader/vectorbt/pandas • Automation: Python APIs or Pine Script (based on deployment preference) Timeline: • Backtesting & reporting: 5–7 days • Final automation code: 2–4 days Budget: ₹25K – ₹90K depending on number of strategies and automation scope. Happy to review your datasets and start immediately. Shubham Sharma

@mubashir021

I'm excited to help you backtest and code your momentum-based trading strategies. With your historical price data and technical indicators in hand, I can quickly dive into the analysis. My plan involves using Python with libraries like Pandas for data manipulation, NumPy for numerical analysis, and Matplotlib for visualizing performance metrics. For the live code, I recommend sticking with Python for its versatility and ease of debugging. The project will be broken down into the following phases: 1. **Backtesting (5 days)**: I will rigorously test each strategy, providing key performance metrics, equity curves, and trade statistics. This will also include iterations based on your feedback. 2. **Coding (5 days)**: Once the logic is solidified, I will develop a production-ready script that reproduces the results accurately and is well-commented for future adjustments. 3. **Documentation & Handover (4 days)**: I will prepare a concise read-me file that explains installation and usage, ensuring a smooth transition for you. With a total of 14 days for completion, I am committed to delivering high-quality work that meets your expectations. Let's get started!

@kibetk6

Having worked extensively in financial technologies and with a solid background in both MQL, Python and Pine script, I am primed to bring tremendous value to your project. Backtesting and coding momentum strategies is right up my alley. I appreciate the thoroughness of your project description and have no doubts about my ability to execute it. For the backtesting phase, I intend to take advantage of Python's wide array of libraries specifically suited for data analysis in finance such as Pandas, NumPy, and Matplotlib. This will also ensure that when I move into the production phase of coding the strategy, everything remains streamlined. When it comes to the actual coding aspect, whether you opt for Python, Pine Script or another language, I am versatile enough to adapt and deliver what you need. My code will not only replicate your tested results flawlessly but will be accompanied by extensive comments, allowing easy parametric adjustment on your end. I'm committed to delivering a comprehensive read-me explaining installation and usage too.

@ashwind0112

For this project, I would break the work into phases to ensure the strategy logic is properly validated before automation is built. Tools I would use: * Python * pandas / numpy for data analysis * vectorbt or backtrader for backtesting * matplotlib / plotly for visualizations * quantstats for performance reporting * ta-lib or pandas-ta if indicator recalculation is needed Workflow: 1. Review and validate the historical price and indicator data 2. Implement and backtest each momentum strategy 3. Generate performance reports including: * Total return * Sharpe ratio * Max drawdown * Win rate * Profit factor * Trade statistics * Equity curve and drawdown charts 4. Iterate on parameters and improve the logic if required 5. Build a production-ready automation script 6. Deliver clean, commented code with a README for setup and usage Estimated timeline: * Data review and setup: 4–8 hours * Backtesting and reporting: 2–5 hours per strategy * Strategy refinements and iterations: 6–15 hours * Production-ready automation script: 8–20 hours * Documentation and handover: 2–4 hours Estimated total: * Small project: 20–35 hours * Medium project: 35–70 hours * Larger multi-strategy systems: 70+ hours The final deliverable will reproduce the tested results on the same dataset and will be structured so parameters and logic can easily be adjusted later.

@gurpreets058

I’m Gurpreet Singh, a professional freelance developer based in New Delhi, with 10+ years of experience in delivering secure, scalable, and high-performance digital solutions. I help startups and businesses turn their ideas into powerful, market-ready products. ? What I Can Do for You Mobile App Development (Android & iOS) Desktop Software Development (C#, Java, .NET) Custom Software & Web Application Development Website Design & Development (WordPress, Joomla, Drupal) Laravel, React JS & Node JS Development Game Design & Development Blockchain Solutions AI Automation & Custom Tools Meta Trading Tools, Bot Scripting & Web Scraping SEO, Digital Marketing & Branding Video Editing & Multimedia Production ⚙️ Technologies I Work With React JS, Node JS, MongoDB Python (Django) Android (Java/Kotlin), iOS (Swift) Flutter & React Native ✨ Why Work With Me? ✔ 10+ years of proven industry experience ✔ Modern, scalable & cost-effective solutions ✔ Creative and experienced development approach ✔ Transparent communication & smooth workflow ✔ Secure, optimized & future-ready technology ✔ On-time delivery with dedicated support ✔ Flexible pricing (open to discussion) ? Let’s Work Together If you’re looking for a reliable freelancer who can bring your ideas to life and deliver high-quality results — I’m here to help. Let’s build something amazing together ?

@pallavbansal2

Hello, I see you’re looking for a robust backtesting framework for your momentum-based stock trading strategies, which is critical to ensuring the reliability of your trading logic before deployment. A key challenge will be effectively interpreting the performance metrics and drawdown statistics to refine the strategies adequately. I would start by implementing a Python-based solution using libraries like Pandas and Backtrader for backtesting, as they provide flexibility in handling various trading strategies and technical indicators. This approach allows us to easily visualize equity curves and trade statistics while ensuring reproducibility with your historical data. For the live script, I recommend transitioning to either a streamlined Python codebase or utilizing Pine Script if you prefer integration with TradingView for real-time execution. As requested, I can share my toolset details and timeline: I estimate approximately one week for rigorous backtesting followed by another week for final coding once we finalize the strategy logic. Do you have a preferred programming environment set up already, or are you open to suggestions?

@zeinabm23

Hello, I’m very interested in your project. I have experience working with quantitative trading systems, momentum strategies, and automated backtesting workflows in Python. For this project, I would use: Python + pandas for data handling vectorbt / backtrader for strategy backtesting matplotlib / plotly for equity curves and performance visualization Clean modular scripts for automation and future parameter adjustments What I will deliver: Full backtest for each strategy Performance metrics (returns, Sharpe ratio, drawdown, win rate, trade statistics, etc.) Clear equity curve and reporting Iterative optimization if needed Production-ready automation script Well-commented code Short installation and usage documentation Estimated timeline: Backtesting & analysis: 4–6 days Automation & final production code: 3–5 days I can complete the full project for €350. Looking forward to discussing the strategies and datasets with you. Best regards,

@Prynicp

Hi, We’re Prynic Innovation, and we’d be excited to help you validate and automate your momentum-based trading strategies. Our approach will begin with a rigorous backtesting phase using Python-based tools such as Pandas, NumPy, and backtesting frameworks like Backtrader/vectorbt to analyze performance metrics, drawdowns, win rates, trade statistics, and equity curves in a clean and interpretable format. Once the strategy logic is finalized, we’ll develop a production-ready script that: Reproduces the tested results accurately Includes clean, well-commented code Allows easy parameter adjustments Comes with setup and usage documentation We focus on accuracy, reproducibility, and scalable architecture to ensure your strategies are reliable both in testing and automation. Estimated timeline: Backtesting & analysis: 3–5 days Optimization & iteration: based on feedback Final automation script: 2–4 days Looking forward to reviewing your datasets and building a reliable trading automation workflow. Thanks Prynic Innovation

@arvindk151

I’ll use Python with Pandas + VectorBT/Backtrader for fast multi-strategy backtesting and PyFolio/QuantStats for analytics (CAGR, Sharpe, max DD, win rate, expectancy, exposure, equity curve, trade logs). Workflow: 1. Validate & normalize your OHLCV + indicator dataset 2. Implement each momentum strategy with parameterized rules 3. Run walk-forward/backtests + sensitivity analysis 4. Deliver performance report with equity curves/trade stats 5. Convert validated logic into production-ready automation script (Python/Pine Script depending on execution target) Tech Stack: * Backtesting: Python, Pandas, VectorBT/Backtrader, NumPy * Analytics: QuantStats/PyFolio * Live Automation: Python + Broker API/Webhooks/TradingView if needed * Optional: Pine Script strategy replication for TradingView Estimated Timeline: * Data validation + strategy implementation: 1–2 days * Backtesting + optimization iterations: 2–4 days * Production automation + documentation: 1–2 days Final deliverables: * Reproducible backtest environment * Clean modular codebase * Strategy configs/parameter controls * Equity curve + metrics report * README for setup/execution * Optional live execution integration

@Nareshkumarsfdc

My name is Naresh, and while my expertise may not directly align with your project in terms of technical specialization, my experience as a software architect brings a unique set of skills to the table that would greatly benefit your backtesting and coding needs. With over 12 years working with complex data systems, I'm intimately familiar with the meticulous process of designing, implementing, testing, and maintaining high-performing software solutions. Much like your own requirement for this project, my work also hinges on accuracy, performance metrics, system integrations, and scalability - all skills entirely transferable to your backtesting and code automation ambitions. While I don't have prior experience in the exact tools you've mentioned(Python or Pine Script), given my technical fluency in multiple languages and frameworks like Apex & Lightning web component(LWC), I can readily adapt to the chosen language or recommend suitable alternatives if necessary.