Python Quantitative Trading Algorithm Development

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@webmasters486

Hey, I’ve reviewed your project and understand you’re looking to turn raw quantitative trading ideas into a production ready Python system covering data ingestion, signal logic, robust backtesting, and live broker API deployment. The focus will be clean architecture, reproducible results, and performance aligned with your Sharpe and latency targets. I can build a modular strategy framework using pandas, NumPy, SciPy, ta-lib, and a backtesting engine such as Backtrader or Zipline. This will include data cleaning, feature engineering, risk controls, position sizing logic, walk forward testing, and detailed performance reports with equity curves and drawdown analysis. Once validated, I’ll deploy it to a live environment with logging, error handling, and lightweight monitoring. You’ll receive fully documented source code, backtest reports, and a concise implementation guide for future iterations. Let’s connect so we can define metrics, broker API, and timeline for first strategy release. Best regards, Muhammad Adil Portfolio: https://www.freelancer.com/u/webmasters486

@vishal1145

Your strategy will fail in live trading if the backtest doesn't account for slippage, partial fills, and API latency during volatile market conditions. I've seen algorithms that looked perfect on historical data blow up within hours of going live because they assumed instant execution at mid-price. Before architecting the solution, I need clarity on two things: What's your expected trade frequency - are we talking 5 trades per day or 500? And what broker API are you using, because Alpaca's rate limits are very different from Interactive Brokers' TWS, which directly impacts how we handle order management and real-time data feeds. Here's the architectural approach: - PANDAS + NUMPY: Build a vectorized backtesting engine that processes 10+ years of tick data in under 60 seconds, with proper forward-fill logic to prevent look-ahead bias that inflates backtest performance. - SCIPY + MACHINE LEARNING: Implement walk-forward optimization with cross-validation to avoid overfitting, using statistical tests like the Sharpe ratio confidence interval and maximum drawdown Monte Carlo simulations. - BACKTRADER + BROKER API: Design an event-driven execution layer with order state machines that handle rejected orders, partial fills, and connection drops without losing track of positions. - UNIT TESTING + LOGGING: Write pytest suites covering edge cases like market gaps, halted symbols, and negative account balances, plus structured logging that captures every order decision for post-trade analysis. I've built 4 production trading systems that processed $2M+ in daily volume without manual intervention. Two questions though - what's your risk tolerance for max drawdown, and do you need this to run on cloud infrastructure or local hardware? Let's schedule a 20-minute call to walk through your signal logic and make sure the architecture can handle your specific market conditions before we commit to a build timeline.

@citijayamala

1. I am an expert in Python, Machine Learning, Data Analysis, R programming, R markdown as well. I have done many projects in Data mining and Machine learning projects. I have handled many data analysis part using R, Python based on the project requirement. I provide codes, writing reports as well. 2. Have done many projects. I read your project and sure I can handle your project. 3. Your project will be delivered on time with high standard 4. Assistance will be provided with number of clarifications until client satisfaction 5. I will provide assistance even after the payment. And will maintain data (content) security.

@Shrutibimpl

Hello, 1) Which specific broker API are you planning to use for live execution? 2) What asset classes will the system primarily trade (e.g., equities, forex, crypto)? 3) Do you already have a preferred historical data source for the initial backtesting? I will build your quantitative trading system using Python, ensuring the code is modular and easy to update. I will use popular data processing and technical analysis libraries to transform your raw ideas into mathematical signal logic. For the simulation phase, I will implement a standard FOSS backtesting framework to run thorough historical tests and walk-forward validation. I will also develop a dedicated risk management module to handle position sizing and automated exit triggers. Finally, I will integrate the system with your chosen broker API for live execution and build a simple dashboard to monitor performance and system health in real time. Thanks, Bharat

@AlexNdungu

I’m confident enough in building and stress-testing quant systems that I’m happy to review your raw idea and sketch a clean architectural outline before you award the bid. Translating trading logic into production-grade, testable code is what separates hobby scripts from deployable systems and that’s where I focus. Here’s how I would approach your project end-to-end: First, architecture & data layer Build a modular pipeline for historical data ingestion (CSV/API/proprietary feeds) Implement structured data cleaning, normalization, and feature engineering Design reusable signal modules so strategy logic can evolve without rewriting core infrastructure Second, strategy & research layer Develop signal logic using pandas, NumPy, SciPy, ta-lib (or vectorized custom indicators where needed) Implement realistic assumptions: slippage, commissions, liquidity filters Add walk-forward validation and out-of-sample testing to avoid overfitting Refactor into clean, modular Python packages with docstrings and unit tests Deliverables you’ll receive: Clean, version-controlled Python source code A concise implementation guide explaining environment setup, dependencies, and extension points I care deeply about avoiding curve-fitting. If the Sharpe target and latency constraints are realistic, we’ll engineer toward them systematically — not by over-optimizing noise.

@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.

@prashanpatel557

Hello, I can translate your raw quantitative ideas into a production-ready Python trading system with clean architecture, rigorous backtesting, and broker-ready deployment. Approach 1. Research & Data Layer • Historical data ingestion + cleaning (corporate actions, missing data handling) • Feature engineering (technical, statistical, regime filters) • Vectorised signal logic using pandas/NumPy 2. Backtesting Framework • Built in Backtrader or a custom event-driven engine • Realistic slippage, commissions, position sizing rules • Walk-forward validation + out-of-sample testing • Metrics: CAGR, Sharpe, Sortino, max DD, rolling stats 3. Risk & Execution Layer • Dynamic position sizing • Exposure caps + drawdown guardrails • Broker API adapter (modular for IBKR, Zerodha, etc.) • Structured logging + error recovery Deliverables • Modular, well-documented Python codebase • Reproducible backtest notebook + performance report • Config-driven parameter tuning • Deployment guide for VPS/live trading Code will be clean, unit-tested, and designed for rapid iteration. Ready to discuss your benchmark target and timeline so we can structure milestones accordingly.

@Algokage

Hello, I can efficiently develop your fully-automated quantitative trading system using Python, focusing on end-to-end algorithm development, including data ingestion, signal logic refinement, robust backtesting, and live execution preparation through broker APIs. I’ll leverage tools like pandas, NumPy, SciPy, ta-lib, and backtesting frameworks such as Zipline or Backtrader to ensure clean, modular, and well-documented code. My approach includes advising on data cleaning, feature engineering, position sizing, and risk controls to meet your performance metrics. With 5+ years of experience in quant trading development, I’ll deliver Python source code, backtest reports, and an implementation guide. Send a message to discuss further or see samples of similar projects. Thanks, Adegoke. M

@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

@bhartimalu

Hello, I am excited about the opportunity to develop your quantitative trading algorithm. - I have extensive experience in building automated trading systems using Python. - My plan includes: 1. Requirement analysis 2. Algorithm design 3. Development and backtesting 4. Deployment and monitoring - I will use libraries like Pandas, NumPy, and backtrader for efficient development. - I propose a timeline of 4-6 weeks with regular updates via your preferred communication channel. Choose me for my dedication to quality and proven track record. What specific trading strategies are you considering? Do you have any preferred data sources for backtesting? Regards, Bharti M.

@Hemantt841

I understand you require a Python developer to transform your raw quantitative trading ideas into a fully automated system, including data ingestion, signal logic development, backtesting, and live execution via a broker API. Your emphasis on clean, modular code with thorough documentation and unit tests, alongside the use of quant tools like pandas, NumPy, and a backtesting framework, is clear. With over 15 years of experience and more than 200 completed projects, I specialize in Python development, API integration, and data analysis, making me well-suited to handle your requirements. My background includes building robust, production-ready systems that incorporate complex data workflows and ensure maintainability, which aligns with your need for rapid iteration and market-ready risk controls. For your project, I will start by ingesting and cleaning your proprietary data, then implement and refine the signal logic using pandas and NumPy, followed by rigorous backtesting with a framework like Backtrader. I’ll provide reproducible backtest reports and ensure the final model meets your Sharpe ratio and latency criteria before deploying it with logging, error handling, and monitoring. A realistic timeline for this end-to-end delivery would be 4 to 6 weeks. Let’s discuss your project details and timeline to get started on building your trading algorithm.

@murtuza90

With over 13 years of ?????? experience under my belt, I am well-versed in ?????? ???????????? and have developed a knack for making complex ideas into streamlined, production-worthy solutions. Your innovative project is right up my alley, and I've got a keen sense for clean, modular code with comprehensive docstrings and unit tests - qualities you've expressed are essential for this project's success. I'm quite comfortable working with the quant tools you've mentioned: ?andas, ?umpy, ?ciPy, ta-lib - and have hands-on experience with backtesting frameworks including Zipline and Backtrader. Beyond just coding proficiency, I bring with me comprehensive knowledge of data cleaning, feature engineering, position sizing, and risk control strategies gleaned from working on similar quantitative trading projects. Performance-wise and to ensure smooth execution once live-trading commences, I will maintain diligent logging practices, implement error-handling measures for stability, and build basic monitoring dashboards to keep tabs on the operation. My final deliverable will also include a meticulous guide that outlines setup instructions and any dependencies where necessary. Rest assured, I'm capable of delivering upon your expectations of high reliability beyond defined benchmarks, reproducible backtest results,s and efficient execution speed.

@InjectAI

Hello, Your project aligns perfectly with my experience in quantitative trading system development. I have built end-to-end Python trading pipelines covering data ingestion, signal engineering, backtesting, and broker API deployment. Please see my profile to review related quant and algorithmic trading projects I’ve completed. ................. Proposed Approach ................. • Clean and normalize historical data (corporate actions, missing data handling) • Feature engineering using pandas, NumPy, SciPy, TA-Lib • Modular signal logic with configurable parameters • Backtesting via Backtrader (or preferred framework) • Walk-forward and out-of-sample validation • Risk controls: position sizing, volatility targeting, drawdown limits • Performance reporting (Sharpe, equity curve, drawdown, trade stats) • Live deployment with broker API, logging, monitoring, and error handling ................. Deliverables ................. • Clean, well-documented Python source code with unit tests • Reproducible backtest reports • Configuration files for rapid iteration • Implementation guide (setup, dependencies, extension instructions) I prioritize reproducibility, deterministic results, and execution efficiency suitable for live trading. Let’s discuss benchmarks and infrastructure details to move forward. Regards

@nathanc97

Hello. I’m highly confident in delivering this project, as I’ve successfully worked on similar systems in my previous company. I approach projects not just as a freelancer, but as a software engineer with a strong focus on system architecture, scalability, and performance — not only functionality or UI. I don’t work solely for payment; I genuinely care about the success of the business behind the project. Contributing to real growth and measurable results is something I truly enjoy as a software developer. If you choose to move forward with me, I will treat this as a business-driven initiative and do my best to ensure it delivers real value. I look forward to your response.

@lokdeshwark

Being an experienced Python developer, my focus on clean and modular code coupled with thorough documentation and unit testing aligns perfectly with your quant trading algorithm project. I've spent adequate time working with pandas, NumPy, SciPy in tandem with backtesting frameworks such as Zipline and Backtrader. My projects usually involve handling and analyzing large sets of data effectively which become essential in developing quantitative models for trading. Moreover, I’m well-versed in ta-lib and other similar tools that will prove to be vital in this project. The level of detail and significance you attach to performance metrics, risk controls, and position sizing matches my own approach to algorithmic trading. I’ve successfully created similar strategies, conducted precise walk-forward testing, and deployed algorithms into live trading environments enforcing extensive logging, error handling, and monitoring dashboards. In summary, my expertise in data analysis, profound understanding of Python libraries crucial for this project combined with my proficiency in software architecture are a right fit for your quant trading system development needs. With me on your team, you can expect clean codebase delivered on agreed deadlines to ensure reproducible results and performance above the benchmark

@quickleadsindia

As an established full-stack tech company, we specialize in providing tailor-made web solutions to transform your digital presence and increase your competitive edge. While our key services aren't specifically about quantitative trading, our immense experience in Python aligns to take on this project- to leverage the power of backtesting to deliver competent and profitable strategies that meet your acceptance criteria. Let me assure you that our skills extend far beyond just basic programming; they encapsulate comprehensive documentation, modular coding, and unit testing - essentials for rapid iteration and superior performance. Our knowledge in tools like pandas, NumPy, ta-lib combined with backtesting frameworks such as Zipline or Backtrader adeptly intersects with your project requisites. We understand that your final goal is to have a highly efficient and seamless trading system. Our capacity in integrating error handling systems along with logging reflects our dedication towards providing top-notch system. Moving forward, risk control strategies will be deployed efficiently to weed out any viable deviations. Partnering with us guarantees you a robust model characterised by clean code, well-documented procedures, in-depth testing, timely delivery along with the ability for scalability and functional efficiency

@jazzyhuang

Building a quantitative trading system that actually performs in live markets requires more than just backtests—it needs robust signal logic, proper risk controls, and clean architecture for fast iteration. My approach focuses on modular Python code using pandas, NumPy, and Backtrader for backtesting, with comprehensive unit tests and documentation. Walk-forward validation ensures your strategy isn't curve-fit to historical data. Deployment includes error handling, logging, and monitoring dashboards so you can track performance in real-time. Deliverables: full source code, backtest reports with Sharpe analysis, and implementation guide. ₹25,000, 10 days.

@pratikshaz2410

I am a Data Analyst with strong expertise in Python, NumPy, Pandas, and SQL, specializing in quantitative data analysis and statistical modeling. I have hands-on experience working on real-world analytical projects including: • Ecommerce Churn Analysis • Bank Transaction Fraud Detection • Social Media Engagement Analytics For this project, I can: ✔ Perform quantitative data analysis using Python ✔ Apply statistical techniques and mathematical modeling ✔ Clean, process, and validate large datasets ✔ Deliver well-structured, documented, and optimized code ✔ Provide clear insights and visualizations I focus on accuracy, performance, and timely delivery. I am confident I can complete this project within 7 days with high-quality results. Looking forward to discussing your requirements in detail. Best regards, Pratiksha