10-Year NYSE/NASDAQ Backtest

@HiraMahmood4072

Hello, I have over 7 years of experience in Data Processing, R Programming Language, Statistical Analysis, and Statistics. I have carefully read the requirements for the 10-Year NYSE/NASDAQ Backtest project. To fulfill the project requirements, I will start by sourcing the historical data from reliable sources, ensuring it is clean and adjusted for corporate actions, splits, and dividends. I will then structure the data into a consistent format with accurate symbol mapping to prevent any disruptions in the series due to delistings, mergers, or ticker changes. The final deliverable will be in a user-friendly format such as CSV files or a lightweight SQL/Parquet database, accompanied by a detailed README document explaining field definitions and adjustment methodologies. I am confident in my ability to provide a comprehensive and accurate dataset for your backtesting needs. Please connect with me for further discussion on how we can proceed with this project. You can visit my Profile: https://www.freelancer.com/u/HiraMahmood4072 Thank you.

@ambar

Do you need delisted stocks included or only active ones? Should adjustments be dividend + split, or split only? Is daily enough or do you want option to extend later? I went through your description carefully. Hi, this is Ambar Shome, I work independently but also run Shome & Associates for larger projects. I’ve done similar data prep work before. No problem. I’ll prepare a clean dataset you can load directly into pandas. No messy joins later. Data will be pulled from 2–3 sources and matched, so gaps or bad rows are reduced. I’ll keep one consistent symbol map so ticker changes don’t break your series. Delisted tickers can be included (important for backtests). Adjustments will be applied properly (splits + dividends), and I can also keep a raw version side by side. Sometimes useful. Files — CSV for easy use, and Parquet if you want faster load. Data split by year so it’s not heavy to open. Dates normalized to NY close. Small thing but avoids issues later. I’ll also run a small check (random symbols) against source data before delivery. README will be simple — fields, adjustments, any gaps. One small thing we can decide… how strict you want missing data handling. Should I forward-fill small gaps or leave them as-is? Yes/No

@mobimubasir

Hey, I understand the need for clean, efficient code that's easy to import - whether CSV or using a lightweight SQL or Parquet database format. Enabling you to effortlessly gain valuable insights from the accumulated stock data throughout the past decade is within my grasp. With your vision and my expertise, we'll overcome any challenges posed by delistings, mergers, or ticker changes while providing you with a comprehensive dataset that will fuel your models and backtests effectively. Let's collaborate on this exciting project; together we will exceed expectations!

@softsolution2000

I understand you need a comprehensive 10-year backtest on NYSE/NASDAQ stock market data with daily OHLCV prices, adjusted for corporate actions, splits, and dividends. Consistent symbol mapping and clean delivery format are essential. I can provide a reliable data pipeline in Python for accurate and complete data. Once confirmed, I'll sign off. Budget can be adjusted as needed. Please review my 15-year-old profile to see my experience. Let's discuss the details and get started on this project.

@maximg10

⭐Hello, I’m ready to assist you right away!⭐ I believe I’d be a great fit for your project since I excel in data processing, Python, statistical analysis, and SQL. With a solid background in data management and statistical analysis, I'm confident in delivering accurate and complete datasets for your 10-Year NYSE/NASDAQ Backtest. Leveraging my expertise, I will ensure the dataset is structured with daily OHLCV prices fully adjusted for corporate actions, splits, and dividends. The consistent symbol mapping will prevent any disruptions due to delistings, mergers, or ticker changes. The final deliverable will be in a single, tidy format, making it easy for you to import into pandas. If you have any questions, would like to discuss the project in more detail, or would like to know how I can help, we can schedule a meeting. Thank you. Maxim

@Nada100200

I can deliver a clean, analysis-ready 10-year NYSE/NASDAQ dataset with fully adjusted OHLCV (splits, dividends, and corporate actions handled correctly) along with robust symbol mapping to preserve continuity across ticker changes, mergers, and delistings. I’ll build this using a reliable Python pipeline (pandas + vetted data sources like CRSP-compatible feeds or high-quality APIs where applicable), ensuring consistent schema, timezone alignment, and no missing structural fields. The final output will be provided in your preferred format (CSV, Parquet, or lightweight SQL), optimized for immediate ingestion into pandas with zero cleanup required. I’ll also include a concise README detailing field definitions, adjustment methodology, data lineage, and any caveats, plus a validation summary comparing sampled records against raw exchange data for accuracy. The focus will be completeness, consistency, and reproducibility so you can plug it straight into your backtest models without friction.

@FaizalShaik80

Hi, hope you are well. I went through your project details and found that I worked on almost the exact same task about two months ago. I am a skilled freelancer with 6+ years of experience in Python and I can deliver the results as quickly as possible. Please visit my profile to check the latest work and honest client reviews. Connect in chat to discuss details and next steps. Talk soon.

@AZTURK1

Hi there, I will prepare a clean, fully-adjusted 10-year OHLCV dataset for NYSE/NASDAQ designed to drop straight into pandas , I have production experience building Python pipelines that handle splits, dividends and corporate actions so series continuity is preserved. - Deliverable 1: Daily OHLCV CSV + Parquet files (option for lightweight SQLite) with prices adjusted for splits/dividends and corporate-actions reconciliation. - Deliverable 2: Consistent symbol mapping table and resolved time-series combining delistings, ticker changes, mergers and CUSIP/permno crosswalks. - Deliverable 3: README with field definitions, adjustment methodology, known caveats and import examples (pandas). - Quality control: validation tests, spot-check reconciliation against exchange snapshots, and rollback notes; staged delivery so you can confirm before final sign-off. Skills: ✅ Python ✅ SQL / Parquet ✅ Data pipeline & adjustment methodology ✅ pandas import / delivery-ready formats ✅ Data validation, reconciliation, completeness checks Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I’m available to start immediately , Do you have a preferred universe (all NYSE+NASDAQ tickers, only current listings, or a provided ticker list) and which final format do you prefer: CSV, Parquet, or SQLite? Best regards,

@danielboggs

Hello, I’ve read your brief and can deliver a clean, fully-adjusted 10-year NYSE/NASDAQ historical dataset ready for pandas. I build reproducible Python pipelines that correct for splits, dividends and corporate actions, normalize tickers across delistings/mergers, and export tidy CSV/Parquet or a lightweight SQLite/Parquet DB with clear schemas. I’ll validate against exchange spot checks and include a short README describing fields, adjustment methodology, symbol-mapping rules, and known caveats. Technically I’ll use a reproducible ETL: fetch raw exchange files, apply corporate-action tables, forward/back-adjust OHLCV, consolidate symbol history with canonical identifiers, and produce per-symbol and consolidated files plus basic QA reports. If you prefer, I can wrap a small DRF endpoint to serve slices for quick checks. Next step: I’ll prepare a sample subset (e.g., 6 months for 50 tickers) so you can validate loading and reconciliation, then run the full decade once approved. Do you have preferred canonical identifiers (CIK, ISIN, or exchange+permanent ID) for symbol mapping, or should I build the canonical mapping from exchanges and corporate-action records? Best regards, Daniel

@huonghy89

Hi, How are you? I’m very happy to submit my bid for your project, as my skills and experience align well with your requirements. I am very familiar with leading trading platforms, including MetaTrader 4 (MT4), MetaTrader 5 (MT5), TradingView, Thinkorswim, Robinhood, and eToro. Additionally, I am proficient in advanced analytical and algorithmic trading tools such as TrendSpider, TC2000, NinjaTrader, QuantConnect, AlgoTrader, and Amibroker. Over the years, I have developed expertise in: • Algorithmic trading and strategy development • Technical analysis and indicator creation • Backtesting and optimization of trading strategies • Automation using Expert Advisors (EAs) and trading bots • Integration of APIs for trading systems I have successfully worked on projects involving custom indicator development, automated trading systems, and data-driven trading strategies across forex, stocks, and crypto markets. I am confident in delivering high-quality results tailored to your project needs. If you send the message, we can discuss the project more. Thank you.

@ffulb

Consistent symbol mapping across ticker changes is the tricky part here - I've handled this exact challenge in my Trading Strategy Optimization project where I backtested across 25 trading days with clean data pipelines. I'd build a Python pipeline using pandas that fetches from Alpha Vantage or Yahoo Finance API, handles corporate actions automatically, and outputs everything as clean CSV or Parquet files ready for your models. The approach would be to pull daily OHLCV data, cross-reference symbol changes through SEC filings, then create a unified dataset with proper adjustment factors. I'd include data validation against known benchmarks and document any gaps or methodology quirks in the README. You can check out similar work at ffulb.com. Once I take a look at which data provider API keys you have access to, I can assess the best collection strategy and get the pipeline running.

@durgesh8527

With over a decade's worth of experience as a full stack developer, I have the technical competency and meticulousness necessary for your project. My solid proficiency with Python, SQL, and data management combined with my dedication to delivering clean and scalable codes makes me well-suited for providing you with a well-structured historical dataset that aligns perfectly with your models' requirement. More than just completing tasks, I focus on understanding every aspect of a project to ensure accuracy, quality, and user-friendliness. I will provide you with daily OHLCV prices that are fully adjusted for corporate actions, splits, and dividends while maintaining consistent symbol mapping that can only be achieved when working with an experienced professional like me - ticker changes and mergers will not break the series. Your project calls for an individual who not only boasts specialized skills but also demonstrates efficiency in handling end-to-end projects. And that's what I bring to the table. Additionally equipped with a competent team of professionals specializing in design, graphics, and more. Rest assured your delivery will be on time, accurate, and complete. Let's get started immediately!

@adisonw

Dear Sir, I am thrilled to bid your project. Your dataset requirements are exactly the kind of work that needs careful structure from the start, because for a serious backtest the value is not only in collecting OHLCV history, but in making the data clean, adjusted, and stable across ticker changes, delistings, and corporate actions. I have solid experience working with financial data pipelines, structured data delivery, Python-based processing, symbol normalization, and analytics-ready datasets that are easy to load into pandas without extra cleanup. For this project, I would focus on building a tidy and well-documented historical dataset for NYSE and NASDAQ over the last 10 years, with adjusted price series, consistent symbol mapping, and a delivery format that supports practical research use, whether CSV, Parquet, or a lightweight SQL database. I would also include a clear README covering field definitions, adjustment logic, and any important caveats so the dataset is usable and transparent from day one. One important question I’d like to ask is this: for symbol continuity, do you want the final data keyed primarily by current ticker symbols for convenience, or by a stable internal identifier so mergers, delistings, and ticker changes remain fully traceable in your backtests? Sincerely, Adison.

@ivicab6

With over 6 years of experience as a full-stack engineer, I have developed a high level of expertise in automating business processes and system-to-system workflows, something particularly valuable when dealing with large volume financial data. My proficiency in Python and SQL will directly assist in structuring and cleaning your dataset for an efficient backtest. I specialize in building end-to-end applications with clean, reliable architecture, addressing your need for consistency in symbol mapping even when faced with delistings, mergers, and ticker changes. By leveraging my experience with data pipelines, analytics, and ML components all in Python, I can ensure consistent OHLCV prices fully adjusted for corporate actions, splits, and dividends. Moreover, I'm well-acquainted with producing clear documentations that provide comprehensive explanations such as field definitions, adjustment methodologies and known data caveats as you requested. My commitment to accuracy and completeness guarantees the delivered product's compatibility with your existing workflows while signaling that reconciling the data with raw exchange data won't be an issue.

@MIndlabsAi6

Hello, I am Vishal Maharaj, with 20 years of experience in Python, SQL, R Programming Language, and Data Management. I have carefully reviewed your project requirements for the 10-Year NYSE/NASDAQ Backtest. To ensure accuracy and completeness over the decade, I propose to utilize Python to extract and clean the historical data, ensuring it is fully adjusted for corporate actions, splits, and dividends. I will create a structured database with consistent symbol mapping to prevent disruptions due to delistings, mergers, or ticker changes. The final deliverable will be in a user-friendly format, such as CSV files or a SQL database, accompanied by a detailed README document explaining field definitions and adjustment methodologies. I am eager to discuss this project further with you. Please initiate the chat to explore this opportunity. Cheers, Vishal Maharaj

@Visharaa2024

Hello, I’m confident I can deliver a clean, reliable, and analysis-ready historical dataset for your 10-year NYSE/NASDAQ backtest. I have strong experience in financial data engineering, including building pipelines for OHLCV datasets with corporate action adjustments (splits, dividends) and handling symbol continuity across mergers, delistings, and ticker changes. I will use Python (pandas, NumPy) with a robust data pipeline to source, normalize, and validate the data, ensuring consistency and accuracy across the full time range. The final deliverable will be provided in your preferred format (CSV ), structured for direct ingestion into pandas with no additional cleanup required. I will also include a clear README covering schema, adjustment methodology, symbol mapping logic, and any limitations. Data will be validated through reconciliation checks and spot comparisons against raw exchange data to ensure integrity. I accept the $300 budget and will deliver within 4 days. All work will be executed by me with a focus on accuracy, completeness, and usability for backtesting models.

@netclickgr

I understand you need a clean, 10-year historical dataset of NYSE and NASDAQ daily OHLCV data, fully adjusted for corporate actions, splits, and dividends, with consistent symbol mapping to handle delistings and mergers without breaking your time series. I'll build a Python/pandas pipeline to source adjusted close data from reliable providers, apply proper adjustment methodologies, and map symbols across ticker changes and delistings. The deliverable will be CSV or Parquet files with a README explaining field definitions and methodology. I'll spot-check against raw exchange data to ensure accuracy before you sign off. With a decade building data pipelines and managing $500K+ in ad spend across 50+ clients, I know how to deliver clean, production-ready data. Ready to start — let's discuss your requirements.

@zarminagull189

Hello, I understand you need a clean, fully-adjusted 10-year NYSE/NASDAQ OHLCV dataset delivered ready for pandas with robust symbol mapping and documentation. Solution: - Extract & normalize daily OHLCV adjusted for splits/dividends using Python pipeline - Reconcile delistings, ticker changes, mergers with persistent identifiers - Deliver tidy CSV or Parquet (your choice) + lightweight SQLite/Parquet index - Provide README with field defs, adjustment methodology, caveats Deliverables: - Per-symbol Parquet/CSV files (or consolidated DB) - Symbol mapping table and reconciliation log - README + sample import notebook (pandas) Budget & timeline: $250 fixed, delivery in 7 days. Portfolio (recent data/dev work): https://www.freelancer.com/u/zarminagull189, https://www.freelancer.com/u/zarminagull189, https://www.freelancer.com/u/zarminagull189, https://www.freelancer.com/u/zarminagull189 Best regards,

@eugeneve188

Hi, I’m Eugene, a UX designer skilled in creating intuitive workflows and clear interfaces. For your backtesting project, I’ll ensure the dataset structure is user-friendly and consistent, helping you navigate daily OHLCV prices and corporate actions without confusion. I’ll focus on delivering a clean, well-documented interface and README that clarifies data fields and adjustment processes, making your import into pandas seamless. My goal is to improve the overall usability and ensure your workflow stays efficient. This approach guarantees fewer errors and smoother data reconciliation, enabling you to confidently validate and use the dataset across the entire ten-year span.