Backtesting Framework

@irsolutions

With over a decade of experience in web and mobile development, specifically in Python, I understand the importance of implementing a robust Backtesting framework for your project. Your requirements for loading data from AWS, utilizing Duck DB, and executing Monte Carlo simulations align perfectly with my skill set. I have successfully implemented similar data-driven projects in the past, particularly in the realm of FinTech, where precision and accuracy are paramount. Leveraging my expertise in Python and Duck DB, I have developed complex algorithms and backtesting frameworks that have delivered exceptional results for my clients. I am confident in my ability to meet your project milestones efficiently and effectively. With my experience in Monte Carlo simulations and data manipulation, I will ensure that the backtesting framework is not only functional but also scalable for future iterations. Let's collaborate to bring your Backtesting framework to life within the critical 7-day timeline. I am excited to discuss how we can achieve your project goals together.

@a4logic

Hello, Landing on your project description, I recognize that my extensive experience as a Database Administrator will be invaluable in developing your Backtesting framework. Working with Duck DB and AWS has been part of my everyday experience over the past few years, enabling me to absolutely grasp the inner workings of handling large-scale data and using it efficiently for varying purposes. I have a deep knowledge not only in loading data into Duck DB, but also in precise execution to generate multiple iterations and performing complex operations like Monte Carlo simulations as requested. Additionally, coming from a reputed software company like mine, I understand the importance of leadership, effective communication, and timely delivery. Your emphasis on needing a professional who can execute this intricate task within a week aligns perfectly with my work ethic. Customer satisfaction is our top priority as we strive to create high-quality deliverables that leave our clients impressed. To sum up, my skills in Database Administration and Development combined with my vast experience in utilizing Duck DB for loading and executing business logic greatly favor your project requirements. If chosen for this endeavor, you can be confident not only in the end result but also the smoothness and timeliness of its execution. I look forward to collaborating with you on this impactful project that can yield valuable insights for your business. Thanks!

@gunslinger

Hey, I’ve worked with data heavy systems and Python based pipelines, including handling large datasets, in memory processing, and structuring execution flows similar to backtesting setups. Your approach with DuckDB and S3 makes sense, and I’m comfortable building the pipeline to load data, generate Monte Carlo variations, run the business logic, and write results back efficiently. I’d focus on keeping things clean and scalable so iterations don’t become a bottleneck, especially with memory and compute constraints. The 7 day timeline is tight but doable if scope is clear and we align quickly on the logic. I’m available to start immediately and can help structure both the framework and execution flow so it’s reliable and easy to extend.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Backtesting Framework in Python with DuckDB and Monte Carlo ❇️ Hi My Friend, I hope you're doing well. I just reviewed your project requirements and see you're looking for a Backtesting framework in Python. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for Backtesting frameworks. I will create a robust solution using your data in AWS, implement your business logic, and ensure all outputs are stored correctly. ➡️ Why Me? I can easily build your Backtesting framework as I have 5 years of experience in Python development, DuckDB, and Monte Carlo simulations. My expertise includes data handling, backtest execution, and cloud storage solutions. Additionally, I have a strong grip on data lakes and object storage, ensuring a smooth workflow for your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you examples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Python Programming ✅ DuckDB ✅ Monte Carlo Simulations ✅ Data Handling ✅ AWS S3 ✅ Backtesting Frameworks ✅ Business Logic Implementation ✅ Data Lakes ✅ SQL Queries ✅ Performance Estimation ✅ Object Storage ✅ Data Analysis Waiting for your response! Best Regards, Zohaib

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you need a Python backtesting framework that pulls strategy data from your AWS mart into DuckDB, runs Monte Carlo-based input variations, executes your strategy logic, and writes the results back to S3/Iceberg tables in a structured way. I’ve worked on similar Python data-processing systems and understand the key challenges around in-memory execution, iterative simulation flow, and keeping the framework clean enough to scale from skeleton to full backtest quickly. My approach would be to build this in clear layers: data load from AWS into DuckDB, seed/input preparation, Monte Carlo variation engine, business-logic execution loop with DuckDB I/O, and final output writing to S3/Iceberg, with profiling included so we can estimate memory and CPU needs before full execution. I’d be happy to go through the details and suggest the best technical approach. I have a few questions to get a better understanding: Q1 – What AWS sources are involved for the input data and output writes (Athena, Glue, S3, Iceberg catalog, etc.)? Q2 – Is the pseudo code already detailed enough to translate directly into Python, or are there still strategy rules to be finalized? Q3 – Roughly how large is the dataset and how many Monte Carlo iterations do you expect for the full run? Looking forward to hearing from you.

@HNMN3

With over a decade of experience in Python development and proficiency in data processing, I can assure you of delivering a top-notch Backtesting framework to meet your requirements. My familiarity with Duck DB is a significant asset as it aligns well with the need to load data in-memory and execute backtests efficiently, to ensure optimal performance. Moreover, my in-depth understanding of SQL will be vital in creating the necessary table constructs and loading the derived data into iceberg tables. One essential insight I bring to the table is my proficiency with AWS services which would be vital for this project. I can guarantee a seamless integration of your AWS data mart along with the backtest input/output datasets which are directly needed for executing business logic. Furthermore, I can handle the complex task of constantly generating variations from seed parameters using Monte Carlo methods; my experience here will avoid unnecessary setbacks for you. Partnering with STR Softwares LLP - Elite Python Development Team delivers much more than just coding expertise. Our senior developers, supported by efficient project management, have a track record of fast yet quality deliveries while maintaining 24/7 communication lines.

@jayprakashyadav

With over a decade of experience in software development, I offer you my proficiency in Database Development with expertise in MySQL. Specifically, I am well-versed in Python and SQL - both of which are critical for this project's success. Handling massive datasets is no problem for me; I possess a deep understanding of loading, processing, and querying them efficiently, as well as experience with Duck DB, the core tool needed for your Backtesting Framework. When it comes to executing your business logic for the Backtest, rest assured that my mastery includes dealing with complex code and consciously optimizing memory and CPU usage. Through effective Montecarlo implementation, I can ensure multiple iterations of Backtests based on variations generated from your input parameter seeds. Given our tight deadline of 7 days, my reputation of meeting stringent timelines without compromising quality serves as a testament that you can trust in my delivery. Ultimately, what sets me apart is not just my skills but also my dedication to ensuring robust and performant code while maintaining easy readability and future scalability. My keen eye for detail assists me in thoroughly testing implementations at various stages and minimizing errors before they become problems. Let's get started on building the Backtesting Framework that best fits your unique needs. Thanks, Jay

@umer6316326

Hello, I’ve carefully reviewed your Backtesting Framework project and am writing this bid myself with full confidence in delivering precisely what you need. Your detailed requirements about leveraging DuckDB with Monte Carlo simulations and integrating with AWS data marts are well understood. With extensive experience in Python and DuckDB, I will create efficient in-memory data operations for your backtest framework. I’ll implement Monte Carlo methods to generate input variations and execute your provided business logic, ensuring seamless data flow to and from your S3-based Iceberg tables. I propose starting immediately to deliver the Python skeleton and initial data processing code swiftly, then progressively complete the business logic and full backtest within the 7-day window. Could you please clarify how the pseudocode for the business logic is structured, and what format it will be provided in? Also, do you require the Monte Carlo simulation to be parallelized for performance optimization? Best regards,

@mubeenm5026

I'm Mubeen Khan, the head of Web Crest. We bring over 10 years of experience in creating intelligent and scalable solutions, making us a perfect fit for your backtesting framework project. Our skills in MySQL, Python, Software Architecture and SQL align closely with your requirements and milestones. To begin with, our proficiency in Python is well-suited to commence bolstering your AWS data mart with Duck DB. We will ensure the fetching and organization of data from the mart into Duck DB is seamless while drawing upon our SQL expertise to create efficient table constructs for backtest output storage. Moreover, our prior experience working with AWS will be beneficial when it comes to S3 integration. Furthermore, our team harbors substantial Monte Carlo experience in generating numerous input combinations for iterations - precisely what you require. From data-loading to executing business logic iterations, we understand your need for a robust Duck DB environment that can support both memory and CPU requirements. We have executed similar projects before, and with streamlined execution, we plan on delivering a fully functional backtesting framework within the critical deadline of 7 days. When you choose us at Web Crest, you don't just get developers - you get long-term technology partners who are meticulous about delivering the best-conceived solutions for your business needs.

@temoberishvili

Hello, This is a well-structured requirement, and the stack (AWS + DuckDB + Iceberg + Monte Carlo) is exactly the kind of high-performance backtesting pipeline I’ve built before. The key here is not just coding, but designing an efficient in-memory execution flow that avoids bottlenecks while scaling Monte Carlo runs. I can deliver this in your 7-day window with clear milestones: • Day 1–2: DuckDB integration, AWS data load, parameter seed handling, and framework skeleton • Day 3–4: Monte Carlo engine + pluggable business logic execution layer • Day 5: Sample runs, profiling, and resource estimation (CPU/memory) • Day 6–7: Final pipeline, S3/Iceberg writes, validation, and cleanup The framework will be modular, allowing you to plug in strategies easily, run parallel simulations, and persist both inputs/outputs cleanly. I’ll also ensure efficient vectorized execution and minimal I/O overhead inside DuckDB. I’ve worked with DuckDB, large-scale data marts, and simulation-heavy systems, so I understand the importance of memory planning and iteration speed here. Ready to start immediately.

@juanjosec63

Hi, As a individual developer I can start work right now, I can help in your project focusing on the most important parts like building the Python backtesting framework, loading AWS data mart data into DuckDB in memory, generating Monte Carlo parameter variations from seed inputs, executing the strategy business logic efficiently, and writing backtest outputs to S3 and Iceberg tables. With my expertise in full-stack development and experience working with modern web technologies like Python, DuckDB, AWS data workflows, SQL, Monte Carlo simulation, S3 integration, and data pipeline architecture, I can fix this quickly. I can structure the work exactly around your milestones, starting with the DuckDB loading and Monte Carlo skeleton, then implementing the business logic with sample runs and resource estimation, and finally delivering the complete backtesting framework in a clean and maintainable way within the critical timeline. You can expect clear communication, fast turnaround, and a high-quality result that fits seamlessly into your existing workflow. Best regards, Juan

@OscarMozo

Hi, I can help you with this. I am a developer with extensive experience with automations and integrations. I've helped clients with similar projects. Let me know your interest, Sincerely, Nicolas

@pinkeegupta

Hi, I can help you in implementation but can take time. So are you strict with timing or flexible? Please message for further technical discussion. I bring 9+ years of combined experience in Python development, Data Science, Data Analytics, and Business Intelligence, helping clients turn raw data into meaningful insights and actionable dashboards. My Core Expertise Includes: Node js , React Js, Mongo , Blockchain, crypto currency Python Development: Pandas, NumPy, Scikit-learn, FastAPI, Flask, Django Data Science & Machine Learning: Data cleaning, EDA, predictive modeling, AI/ML solutions Data Analytics: Statistical analysis, reporting, automation, data mining Power BI: Interactive dashboards, DAX, Power Query, data modeling, KPI reporting Databases & Big Data: SQL, NoSQL, SparkML AI & Frameworks: TensorFlow, PyTorch, Cursor, Calude, gemini, nano, chatgpt. I focus on clean code, clear insights, performance optimization, and business-oriented outcomes. I ensure timely delivery and transparent communication throughout the project lifecycle. Let’s connect to discuss your requirements in detail and define the best approach for your project. Looking forward to working with you. Regards, Anju Logical Soft Tech Pvt Ltd, Indore(M.P)

@alizain721

Seven days to build an in-memory DuckDB Monte Carlo backtester that writes to Iceberg is a tight window, and I get why you want it done cleanly and predictably. I know how risky partial designs are when you need accurate resource estimates and repeatable outputs. The best thing about me is I’ve worked on a very similar project recently. I built a Python pipeline that loaded AWS data into DuckDB, generated parameter variations with Monte Carlo, executed vectorized strategy logic per iteration, and persisted results to Iceberg tables on S3 using the Glue catalog. I understand the flow: extract mart tables into DuckDB, create temp objects and views, read seed parameters, synthesize Monte Carlo scenarios, run the business logic with DuckDB I/O per scenario, and bulk-write outputs to Iceberg. Tech stack: Python, DuckDB, pyarrow/pandas, Iceberg via Glue or Spark, S3. Because I’ve done this I can deliver the skeleton, a working sample run, and a final implementation faster and with reliable CPU/memory estimates. Quick questions: what are typical row counts and size of the primary tables and how do you want Iceberg cataloging managed (Glue catalog, Hive metastore, or another)? Do your pseudo business rules depend on external libraries or pure Python? Happy to chat or jump on a 20 minute call to align and start day one. Regards Ali Zain!!

@techsty2014

Hey, I will build your backtesting framework in Python across all three milestones — DuckDB in-memory loading from your AWS mart, Monte Carlo variation generation from parameter seeds, business logic execution with DuckDB I/O, and output to S3 as Iceberg tables. Seven-day delivery. For Monte Carlo at scale, I will batch variations to fit within DuckDB memory limits rather than generating all combinations upfront — runs thousands of iterations without massive memory provisioning. Questions: 1) Approximate dataset size loaded into DuckDB — gigabytes? 2) How many input seeds, and roughly how many variations per seed? 3) Iceberg tables already configured, or do I set up the catalog? Looking forward to talking through the details. Faizan

@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.

@DngPhgNam

⭐⭐⭐⭐⭐ ✅Hi there, hope you are doing well! I have experience building Python frameworks for financial backtesting that efficiently loaded data into DuckDB for in-memory processing and seamlessly executed Monte Carlo simulations. From my experience, the key to success in this project will be designing efficient data flows and structures to optimize memory and CPU usage during Monte Carlo iterations. Approach: ⭕ Load data from AWS data mart into DuckDB in-memory tables. ⭕ Create necessary DuckDB objects and data structures. ⭕ Implement Python skeleton for parameter seed reading and Monte Carlo variation generation. ⭕ Integrate business logic execution with I/O operations in DuckDB. ⭕ Develop output writing functionality to S3 as Iceberg tables. ⭕ Estimate resource usage and optimize for full backtesting workload. ❓ Could you clarify the size of the datasets and expected number of iterations? ❓ Would you prefer the business logic implementation fully integrated or modular? ❓ Can you specify the format of the pseudocode for the business logic? I am confident in delivering this critical project within the 7-day timeline with high-quality, scalable code that meets your requirements. Best regards, Nam