Backtesting Framework

@kamran2012

With over 12 years of experience in full-stack development, I bring a cross-functional skill set that perfectly aligns with your project needs. I have considerable expertise in data processing, database administration and development, with an emphasis on optimizing workflows and automating processes to maximize efficiency - skills that will be critical for your backtesting framework. Not only have I developed Python programs to load large datasets into Duck DB from AWS S3 before executing Monte Carlo simulations, my deep understanding of working with various databases will help ensure the Scaffold to Iceberg tables are built and properly utilized to store the backtesting output. As a result of my work using Duck DB as well Montecarlo readiness and a keen eye for detail, I can also estimate the memory and CPU requirements needed for running the full backtesting operations. It's worth noting too that my experience extends to AI-powered applications, automation systems and cloud infrastructure – all of which can significantly improve your system's performance. Moreover, my principled approach involves proactively identifying potential challenges and offering solutions ahead of time. I'm confident in leveraging my robust skill set and track record of delivering high-quality projects on time to get this critical job accomplished successfully. Let's chat more about your project considerations.

@mohjib092

Hello, I can build a clean and production-ready Python backtesting framework that loads data from your AWS data mart into DuckDB (in-memory), runs Monte Carlo variations, executes the strategy logic, and writes the results back to S3/Iceberg tables. I can start immediately and deliver the first working skeleton within 24 hours. Looking forward to working with you.

@vishal1145

Your Monte Carlo simulation will fail if DuckDB runs out of memory during parallel strategy execution. I've seen this exact pattern crash at 50GB RAM when teams underestimate the combinatorial explosion of parameter variations. Before architecting the solution, I need clarity on two things: What's your expected parameter space size (how many seed combinations × iterations)? And does your AWS data mart use Parquet/Iceberg already, or will we need to handle format conversion during the DuckDB ingestion? Here's the architectural approach: - DUCKDB + ICEBERG: Implement lazy loading with partitioned reads to keep memory footprint under 32GB even with 10M+ backtest rows. DuckDB's Arrow integration prevents unnecessary serialization overhead. - MONTE CARLO SAMPLING: Use Latin Hypercube Sampling instead of pure random to reduce required iterations by 40% while maintaining statistical coverage. I'll generate parameter grids in batches to avoid memory spikes. - PYTHON MULTIPROCESSING: Parallelize strategy execution across CPU cores using shared memory arrays (not pickle serialization) to hit sub-5-second execution per variation on a 16-core instance. - S3 WRITE OPTIMIZATION: Buffer outputs in DuckDB, then batch-write Parquet files to S3 with snappy compression. This cuts upload time by 70% compared to row-by-row inserts. - PROFILING PIPELINE: Milestone 2 includes memory/CPU benchmarking with py-spy and tracemalloc to give you exact EC2 instance sizing before full-scale execution. I've built 4 similar backtesting systems for quant teams, including one that processed 500K strategy permutations in 6 hours on a single r6i.8xlarge instance. The 7-day timeline is tight but achievable if your business logic pseudo-code is complete and your data schema is stable. Let's schedule a 20-minute call tomorrow to walk through your pseudo-code and confirm there aren't hidden dependencies that'll blow the deadline.

@munjalmayank

Dear Customer, Greetings from Microprixs Solution! We are one of the best partner in React, Node, Laravel, Next. We can work on following process: 1. I have a data mart in AWS that has all the needed data for to execute the business logic of my Strategies. 2. Same Datalake also includes the table constructs needed to store backtest output. 3. I will also provide the business logic in pseudo kind of code. 4. Need someone with experience in Duck DB to load all the data in-memory , execute backtest and load the backtest input and output dataset in iceberg tables. 5. Some one with Montecarlo experience would be preferable, since various combinations of inputs need to be generated for multiple iterations of Backtest.

@techsty2014

As an experienced Data processing and Software Architect at Stallyons Technologies, I am confident in my ability to deliver precisely the Backtesting framework you are seeking. For starters, my expertise in AWS will ensure we maximize the potential of your data mart in an efficient and secure manner. Additionally, I've got a strong handle on Duck DB, which means I'm skilled enough to load all your data into an in-memory structure while executing Monte Carlo variations and running business logic for each iteration. Furthermore, my proficiency doesn't end there; I'm skilled with MySQL too. This will help me effectively manage the creation, reading, updating, and deletion operations required to store your valuable backtest input and output dataset in the iceberg tables. My career journey spans over 12 years during which I've successfully completed more than 700 projects, covering everything from design to cloud computing- giving me a well-rounded perspective important for any complex endeavor like this.

@Shrutibimpl

Hello, I will develop a high-performance backtesting framework in Python using DuckDB for efficient in-memory data processing and business logic execution. I will build a pipeline to fetch data from your AWS mart, implement the Monte Carlo simulations to generate input variations, and store the resulting datasets back into S3 as Iceberg tables. My approach includes creating a modular skeleton for data loading, executing your pseudo-code iterations, and optimizing the CPU and memory usage for large-scale backtests. I will ensure the final implementation is fully automated and integrates seamlessly with your existing data lake. 1) What is the total volume of data currently stored in your AWS data mart? 2) Is your Iceberg catalog managed via AWS Glue or another service? 3) Which Python libraries do you prefer for generating the Monte Carlo iterations? Thanks, Bharat

@letswork92

I understand that you require a robust Backtesting framework implemented in Python, leveraging your AWS data mart and incorporating Monte Carlo simulations for multiple iterations. This is crucial for executing your trading strategies effectively. With over 12 years of experience in full-stack development and automation, I have a strong background in working with DuckDB for in-memory data processing and can efficiently manage the required business logic. I am familiar with integrating outputs into Iceberg tables and performing I/O operations within DuckDB, ensuring seamless data flow to S3. To address your needs, I will deliver the project in milestones: starting with establishing the skeleton code to load data and execute the Monte Carlo variations, followed by implementing the business logic and estimating resource requirements, culminating in the final backtest implementation. As time is critical, could you share more about the specific business logic you wish to implement? This will help tailor the solution precisely to your needs.

@chiragardeshna

Hi there, Strong alignment with this project comes from experience delivering Python-based backtesting systems with in-memory processing and Monte Carlo simulations. Clear understanding of the requirement to build a DuckDB-powered framework integrating AWS data lakes, executing strategy logic, and writing outputs to Iceberg tables. Hands-on expertise with Python, DuckDB, and data pipelines ensures efficient data loading, simulation execution, and scalable performance. Risk is minimized through structured milestones, performance estimation, and optimized resource usage for timely delivery. Available to start immediately happy to discuss execution plan and meet your 7-day deadline. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@nareshk44

As an experienced data professional, I can confidently proclaim that this Backtesting Framework project is right up my alley. Not only have my past projects involved working extensively with data lakes and AWS; more importantly, I have vast experience developing sophisticated databases and proficient in Duck DB. My competence will be a critical asset at the early stages when we need to load all your data from the mart into Duck DB efficiently. In terms of data management, I am highly-skilled in Python and have deep knowledge of libraries like pandas and numpy that will ensure a smooth transition of the input datasets from Duck DB to S3 for storage. Additionally, my past work with iceberg tables not only gives me direct familiarity with the process but also ensures that I understand its importance in storing backtest input and output dataset. Embracing any project entails tremendous accountability which is complemented by a diverse skillset like mine: from Data Entry and Data Analysis to Database Development and Software Architecture – I am adept at converging these for successful project execution which is exactly what your project demands. Choosing me would guarantee not just efficient project delivery but an unequivocal commitment towards quality and complete satisfaction on your end. Thank you!

@ffulb

Your backtesting framework needs DuckDB for in-memory processing, Monte Carlo parameter generation, and Iceberg table integration with your AWS data mart. I'd build a modular Python system that loads your data into DuckDB, runs Monte Carlo simulations on your strategy parameters, executes the business logic iterations, and outputs results back to S3. I built something similar with my algorithmic trading bot that processes market data and runs backtests with risk management calculations. My price aggregation engine also handles large-scale data processing across multiple sources, which translates well to your multi-iteration backtesting needs. You can see more of my data processing work at ffulb.com. Ready to start once I can review your AWS setup and pseudo business logic code to confirm the optimal DuckDB schema design. Want to discuss the technical approach?

@UzeeTech

With over 7 years of industry experience, I have built end-to-end solutions not too dissimilar to what you are seeking. My proficiency in Python is prime for building your Backtesting Framework. My familiarity with Duck DB will prove invaluable in not only retrieving but also efficiently loading data from your AWS data mart into Duck DB in-memory stores. Moreover, my exposure to Montecarlo simulations further strengthens my capabilities needed for providing multiple combinations of inputs in iterations for Backtests. My peak moments as a developer come when I can construct a coherent skeleton that can be built upon and expanded within an intricate timeline. I don't just deliver rough sketches, I provide code that can read input parameter seeds from Duck DB, execute Montecarlo to create necessary variations, run business logic for each variation, and finally write output all the while minimizing CPU and memory consumption. Additionally, you would benefit from my propensity for clean, documented and testable code - attributes that lead to scalable, secure and optimized applications that perform seamlessly under pressure. This translates into your final milestone; efficient execution of Backtest. Urgency is required here and my reliable track record asserts that over 98% of my projects meet their deadlines or are delivered ahead of time. Let me ensure you get thorough and efficient implementation in your crucial timeframe.

@Ritesh11gupta

I'd be a great fit for 'Backtesting Framework'. I've worked on very similar requirements before and understand the nuances involved. I work with clear communication, daily updates, and won't stop until you're fully satisfied. Please check my profile for past work and let's chat!