Stock Options Data pull and store it in cloud

@raphaelokekeze

I see you need a solution that pulls real‑time stock options data, processes the average bid, and stores it securely in an AWS environment. I can set up a PHP script that connects to the required API, handles the data processing efficiently on a Linux server, and writes the results to S3 or a DynamoDB table as you prefer. With over 15 years of web development experience, I work extensively with PHP, Linux, and cloud services. My background includes building custom data pipelines, optimizing server performance, and integrating APIs into secure, scalable architectures. I’m comfortable configuring AWS services, managing cron jobs, and ensuring the whole system runs reliably. I propose to deliver the complete solution for ₹6000.43 within 7 days. Let me know if you’d like to discuss any specific API details or storage preferences, and we can get started right away.

@rbtech1984

Hi There!!! ★★★★ (Scalable API-based stock options data pipeline with cloud storage architecture for large-scale terabyte-level datasets.) ★★★★ I have carefully reviewed your project and understand that you need a reliable system to pull options data from the Massive API, process it efficiently, and store it in a scalable cloud environment. Since the data volume may reach terabytes, the focus should be on performance, storage optimization, and future analytics readiness. ⚜ API Integration & Data Extraction (Massive API) ⚜ Scalable Cloud Architecture Design ⚜ High-Volume Data Pipeline Development ⚜ AWS / Cloud Storage Setup & Optimization ⚜ Data Structuring for Analytics Use ⚜ Automation & Scheduled Data Ingestion ⚜ Logging, Monitoring & Error Handling My approach would be to design a robust pipeline using efficient batching, streaming or queue-based ingestion, and store data in a format like Parquet/partitioned storage for analytical performance. I would also suggest a cloud solution like AWS S3 + Glue/Athena or similar depending on cost and query needs. I would be happy to discuss expected data frequency, API limits, and best cloud strategy before starting. Looking forward to working together on this scalable data solution. Warm Regards, Farhin B.

@mayankinnovative

Hi, I reviewed your Stock Options Data Pull and Store in Cloud project and it aligns well with my experience in financial data APIs, Python, Node.js, cloud infrastructure, database design, automation, and large-scale data processing. I have worked on market data platforms, automated data pipelines, financial dashboards, API integrations, and cloud-based storage systems. My focus is on building reliable solutions that efficiently collect, process, store, and retrieve large volumes of market data. I can help with: • Stock & options data integration • Financial API development • Automated data collection • Cloud storage architecture • Database design & optimization • Scheduled data pipelines • Data validation & processing • Reporting & analytics support • Performance optimization • Ongoing maintenance A few questions: • Which data provider/API will be used (Polygon, Tradier, IBKR, Alpaca, etc.)? • What cloud platform do you prefer (AWS, Azure, GCP)? • How frequently should the options data be updated? • What database is preferred for storage? I focus on building scalable, reliable data systems that support future analytics and trading workflows. Thanks

@Jennycreation

Hello sir, I have 17 year experience in stock trading Strategy. I already built trading view pine script for Forex market in this we implement many indicator and complex strategy. in pine script we also set alert for live trading using webbook Api. so our pine script do automation trading. I already work on mt5 and mt4 EA system also. where we build many strategy based on many indicator and already work on Forex Crypto Copy trading strategy So I have 17 year of experience in trading industry with below 1 Trading view Pine Script Strategy Development 2 MT5 and Mt4 Expert Development 3 Automation Trading using Python 4 Copy Trading System Let me know if my experience fit on your project. we can more discuss project on Chat. Please inbox me. Thanks

@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

@saadhey

Greetings, It sounds like you need someone to pull options data from the Massive API and store it in the cloud, with potential data sizes reaching up to a terabyte or more. I can help you with that! My approach would be to first connect to the API and efficiently extract the data, ensuring it's formatted in a way that’s easy to analyze later. For cloud storage, I can recommend solutions like Amazon S3 or Google Cloud Storage, which are great for handling large datasets. With experience in PHP, Linux, and data management, I'm comfortable navigating the challenges of data integration and architecture. I believe in clean code and efficient processes to ensure everything runs smoothly and securely. Let’s chat about your specific needs and how I can assist you in achieving your goals.

@huzaifaa177

Based on your requirements, I can build a scalable data ingestion pipeline that pulls options data from the Massive API, processes it efficiently, and stores it in a cloud environment optimized for large-scale analytics. I will implement a robust Node.js or Python-based ingestion service that handles pagination, rate limits, retries, and incremental updates, ensuring reliable data collection even as volumes grow into terabytes. The data will be structured and stored in an analytics-friendly format such as Parquet or columnar storage to optimize query performance and reduce storage costs. For cloud infrastructure, I recommend AWS as the primary option using S3 for cost-effective storage, combined with services like AWS Glue or Athena for querying large datasets. Alternatively, Google Cloud Storage with BigQuery is also a strong option if you prefer SQL-based analytics at scale. I will design the system to be modular so you can easily extend it for real-time ingestion or advanced analytics later, while keeping the setup maintainable and well-documented.

@nabeeli18

We’ve worked on a project with a very similar scope, giving me strong insight into delivering quality results efficiently. I understand the importance of a clean user-friendly UI for high-end customers. I'd love to chat about your project or walk away with a free consultation. Regards, Nabeel Ismail