Build an Advanced Bot to Discover and Monitor 10M+ Trading-Interested Users

@PINESCRIPTERR

I’ve worked on high-volume data and monitoring systems where source restrictions, entity resolution, search performance, and traceable classifications mattered more than raw scraping volume, and I can show relevant architecture examples privately. I’d design this as independent source connectors feeding a durable event pipeline, not one crawler. Approved APIs, licensed datasets, and permitted public-page collection would publish normalized accounts and content into queues, with raw snapshots stored separately from searchable records. PostgreSQL would manage configuration and workflow state, OpenSearch would support account and content discovery, and ClickHouse could handle high-volume activity analytics. Deduplication and cross-platform entity resolution would use explainable signals, confidence scores, and provenance; uncertain identities would never be silently merged. Topic classification would combine deterministic rules, embeddings, and reviewed model outputs. Alerts would run incrementally against new content, with rate controls, replay, dead-letter handling, source health monitoring, and per-platform freshness metrics. I would not propose bypassing authentication, CAPTCHAs, private groups, platform restrictions, or user privacy controls. Retention, deletion, data-subject requests, and permitted commercial use must be part of the architecture. Regards, Houssame

@sdqtrades

As an AI-driven automation specialist, and a trader myself, I am uniquely equipped to design and develop the comprehensive system you've outlined. My past work includes designing and implementing large-scale data extraction and management platforms, particularly in the financial industry, where I have incorporated unique strategies for carrying out trading scripts and algorithms. Moreover, my skills extend to smart data extraction which will be vital for efficiently processing the social platform APIs for maximum results while remaining compliant with their limitations. Apart from my tech experience, my passion lies in trading. That's why I'm not just about scraping data, but rather understand how important reliable data is in producing accurate trade decisions. I have extensive knowledge of different platforms including Tradingview, Metatrader 4 and 5, Thinkorswim and others that form part of your project's scope. Lastly, I value the confidentiality of your ideas and guarantee absolute ownership of the software and source code once completed. Bearing all these points in mind, I am confident that our collaboration would result in a highly efficient 24/7 search and monitoring engine capable of discovering millions of relevant accounts while classifying them based on their interests accurately. Let's discuss your full requirements so that I can offer specific strategies required to satisfy your project's needs while ensuring quality delivery!

@tnt42010

Hello, I have strong experience designing large-scale data collection, monitoring, search, and alerting systems using Python, distributed workers, APIs, queues, PostgreSQL/ClickHouse/Elasticsearch, and cloud infrastructure. For this project, I would not treat it as a scraper. I’d design a modular platform with: Source-specific collectors using official APIs where available Distributed crawling and scheduling Account classification and deduplication Cross-platform identity matching with confidence scoring Content indexing and full-text/topic search Real-time keyword/topic alerts Scalable storage for tens of millions of records Dashboard for accounts, filters, keywords, and alerts Monitoring, retries, rate-limit handling, and audit logs I also understand that each platform has different API, rate-limit, and compliance constraints, so the architecture should explicitly separate supported API integrations from public-web collection and avoid unrealistic coverage promises. I’d start with an architecture/discovery phase, then build the highest-value sources first and scale out incrementally.

@Hubready

Hello!, I am a Florida-based senior software engineer(frontend, backend, ecommerce, etc) with about 15 years of experience building scalable data systems, automation tools, and trading-related software. I read your project description carefully, and the goal is clear: build an advanced system that can discover and monitor 10M+ trading-interested users without becoming a slow or brittle scraper. I’ve built similar solutions around high-volume data pipelines, market analysis tools, automation, and analytics dashboards, so I understand the speed, accuracy, risk control, and maintainability this project needs. My approach would be: 1) confirm the exact data sources and targeting rules 2) design a scalable collection and filtering pipeline 3) add deduping, monitoring, and alert logic 4) deliver clean documentation so it’s easy to run and improve I’ve handled trading data dashboards, lead discovery systems, large-scale monitoring tools, and financial research automation. I’m the type who catches the small details others miss, and that matters here. Could you please clarify the following questions to help me better understand the project? 1) What are the main data sources, and are there any API or platform restrictions? 2) How do you define a “trading-interested user” for filtering and ranking? 3) Should this be a standalone tool, dashboard, or API-based system? If helpful, I can outline the architecture first so you can see the path before development begins. -James

@emanz03

Rather than force the entire mobile + admin scope into one rushed build, I’d start by locking the first-release user flows and AI actions, then build the cross-platform app, REST backend, authentication, notifications, and admin controls around those agreed workflows. My work covers mobile/web apps, APIs, AI integrations, databases, and deployment. The posted budget works best as Phase 1 for the core MVP; after reviewing the exact AI features, user roles, and third-party integrations, I can define the remaining milestones clearly. Which AI workflow is the highest priority for the first release?

@mayr81

Cora May can build a scalable 24/7 discovery and monitoring engine for trading-focused audiences, far beyond a basic scraper. The system will use modular web crawling plus platform-aware collection (API-first where possible), rate-limit compliance, and robust deduplication/linking to maintain a clean identity graph across X, Reddit, LinkedIn, YouTube, TikTok, Instagram, Facebook, Discord, Telegram, forums, and blogs. Architecture: distributed data ingestion, message-queue based pipelines, real-time processing for keyword/topic detection, and a search-optimized index for querying alerts and content. Data modeling supports tens of millions of records, while a monitoring subsystem continuously tracks account activity and content changes. Deliverables include: scalable connectors framework for adding new sources, database design for account/content/alerts, classification and topic tagging, an alerting workflow for predefined conditions, and a dashboard for managing keywords, accounts, and monitoring status. Emphasis is on technical feasibility, platform limitations, and compliance-safe collection strategies to avoid unrealistic promises.

@maxscender

I can help you build this as a scalable data infrastructure project, not a scraping script. My approach focuses on the core architecture: a modular discovery layer that sources data from platform-specific adapters (Twitter, Reddit, Telegram, etc.), a deduplication and identity-resolution engine to merge cross-platform profiles, and a real-time monitoring pipeline that processes content against your keyword rules to trigger alerts. I prioritize building with rate-limit awareness and compliance guardrails built into each adapter from day one—not as an afterthought—so the system stays operational at scale on 10M+ records. The design will use a distributed queue and a time-series database to handle the ingestion and monitoring load, with a dashboard that lets you filter, manage, and search without bottlenecks. I'm ready to discuss the full technical requirements and define the data schema and adapter APIs that will let you add new platforms easily as the system grows. My focus is on delivering a system that runs 24/7 and scales without crumbling under its own data weight.

@mdabdullahallm5

I can architect and develop this as a scalable distributed monitoring platform rather than a basic scraper, using modular connectors, compliant APIs/crawlers, streaming data pipelines, deduplication and entity-resolution services, scalable storage, search indexing, classification, alerting, and a management dashboard. I’ll design for tens of millions of records with queue-based processing, rate-limit handling, fault tolerance, and easy addition of future sources, while clearly accounting for each platform’s API, access, privacy, and compliance constraints.

@prashanpatel557

Hello, I understand you’re looking to build a scalable 24/7 platform that discovers, classifies, deduplicates, searches, and monitors millions of trading-related users across multiple public data sources, with real-time keyword/topic alerts. I’m a Python Automation Developer with 8+ years of experience in web scraping, API integrations, data processing, Flask dashboards, AI-based classification, and algorithmic trading systems. I can design this as a proper data pipeline rather than a basic scraper, using background workers, scalable storage, search indexing, and modular source integrations. I suggest starting with a focused MVP covering 2–3 sources, account discovery, classification, deduplication, content monitoring, search, and alerts, then scaling progressively toward the 10M+ target. Before quoting the complete system, I’d like to discuss your required platforms, API access, monitoring frequency, expected daily volume, and infrastructure budget. I’m available to discuss the architecture and phased development plan.

@amrkhaled32

This isn't a scraper. At 10M+ accounts, you're building a distributed discovery + intelligence pipeline with crawling, enrichment, classification, deduplication, search, monitoring, alerting, and a database that doesn't fall over when everyone decides to post at once. ? That's the kind of architecture I'd want to discuss before writing a single crawler. I'd approach it as: Discovery → Source adapters → Queue → Extraction → Classification → Entity Resolution → Search Index → Monitoring → Alerts → Dashboard For the backend, I'd consider a combination of Python, PostgreSQL, Redis/Kafka-style queues, object storage, and Elasticsearch/OpenSearch, with containerized workers that can scale horizontally. The important reality check I won't tell you I can magically scrape every platform indefinitely. Each platform has different APIs, authentication requirements, rate limits, robots policies, terms, and levels of public accessibility. Some sources may support official APIs; others may only expose limited public data. Measure discovery rate, storage growth, processing throughput, deduplication accuracy, monitoring latency and infrastructure cost at every stage. I've worked with Python, web scraping/crawling, APIs, Selenium, data pipelines, databases, AWS, Docker and automation, so the individual components aren't the interesting part. now we're talking. ?

@venplura

Hi there! Quick question: are you thinking about real-time alerts within seconds of a post going live, or is a few minutes delay acceptable given the scale we're working with? Regardless, this is definitely something that I feel confident delivering on, given my past experience. I would love to discuss your project further! Looking forward hearing from you. kind regards, Corné

@patricko098

GIVE ME 30 SECONDS TO SHOW YOU WHY I'M THE RIGHT FIT FOR THIS PROJECT. I successfully developed a large-scale social media monitoring system that tracked over 5 million accounts across multiple platforms, delivering real-time insights and alerts. This resulted in a 30% increase in user engagement for my client. I have over 8 years of experience in building scalable systems involving web crawling, data pipelines, and real-time monitoring. My background in designing distributed systems aligns perfectly with your requirements. I understand your goal of creating a robust database of trading-interested users and the need for continuous monitoring and alerts. I would implement a modular architecture that adapts to new data sources and ensures efficient data processing. With my focus on execution, clear communication, and long-term success, I’m confident I can deliver a system that exceeds your expectations. The difference between an average result and an exceptional one is usually decided before the work even begins. Regards Patrick

@mubeenm5026

As the CEO of Web Crest, I’m confident that my team can deliver the high-scale system you're seeking. Our extensive experience with web crawling, social media monitoring, and large-scale databases aligns perfectly with your project requirements. We’ve previously developed systems capable of collecting and processing millions of records while maintaining their quality and integrity. Our proficiencies extend to various programming languages such as Python and Node.js, enabling us to design comprehensive data pipelines that ensure efficient data retrieval without violating API restrictions or rate limits. We are highly adaptable, meaning we regularly keep up with platform changes and restrictions to maintain compliance. We’re also well-versed in cloud technologies like AWS, Google Cloud, and Microsoft Azure – crucial for storing and processing vast amounts of data like what you anticipate. Furthermore, our familiarity with AI and automation tech promises more than a simple web scraper; we deliver systems fueled by advanced algorithms which classify data accurately for precise monitoring purposes. Given the project entails linking the same person across different platforms when possible, our Blockchain skill set can be employed to enhance the process's integrity. We've previously worked with Ethereum, Smart Contracts, and Web3 integrations to create interconnected systems securely.

@amit210279

Hi there, You’re in the RIGHT PLACE! I’ve worked on SIMILAR PROJECTS multiple times and understand how to deliver this EFFICIENTLY and CORRECTLY from the start. While I’m NEW to Freelancer.com, I bring 17+ YEARS OF EXPERIENCE from other freelancing platforms, successfully delivering HIGH-QUALITY PROJECTS and REAL RESULTS for clients. To provide an accurate SCOPE, TIMELINE, and COST, I’d like to ask a few KEY QUESTIONS. Due to Freelancer’s character limit, it’s difficult to cover everything here. Let’s connect in CHAT so I can: • Share RELEVANT PAST WORK • Understand your EXACT REQUIREMENTS • Propose a CLEAR and EFFECTIVE ACTION PLAN I’m confident you’ll find my approach PRACTICAL, TRANSPARENT, and RESULTS-DRIVEN. If you're ready to get this done the RIGHT WAY, I’d be happy to get started. Looking forward to CONNECTING with you. Best regards, Amit Ranjan

@bojans9

Hello, I can help design this as a scalable data platform rather than a basic scraping script. The right approach would be a modular ingestion layer, distributed processing pipeline, deduplication and classification services, searchable storage, and a real time alerting layer so additional data sources can be added later. My background includes Python based machine learning, REST API integrations, Node.js microservices, Docker and AWS deployments. I can use that experience to build the system incrementally and keep the architecture suitable for scaling. Before estimating the full build, I would like to clarify which data sources must be supported in the first milestone, which APIs or licensed datasets are available, and what monitoring latency you expect. I recommend starting with an architecture and working MVP, then scaling discovery and monitoring based on measured throughput and infrastructure costs.

@husainahmed2000

Hi There!!! I understand you need a scalable 24/7 monitoring and discovery engine to collect, classify, and track millions of trading-related accounts across social platforms. I HAVE ALREADY BUILT LARGE SCALE DATA PIPELINES AND DISTRIBUTED CRAWLING SYSTEMS THAT PROCESS MILLIONS OF RECORDS. Here are the services provided for your project: * Distributed data acquisition pipeline handling rate limits and proxies * Cross platform account entity resolution and duplicate detection * Scalable database architecture for storing tens of millions of profiles * Real time keyword and topic alert processing engine * Searchable web dashboard for filtering accounts and managing alerts Let us connect over chat to discuss the technical architecture and requirements further. Best Regards, Hussain Ahmed

@alirashdi26

Hey , I just went through the project description, and I see you are looking for someone experienced in Data Visualization, Market Analysis, Pine Script, Financial Analysis, Risk Management, Backtesting, Financial Markets and Documentation. It instantly reminded me of a client who faced similar challenges, and I knew I had a tailor-made solution for it. Please review my profile to confirm that I have great experience working with these tech stacks. While I have few questions: • Is there anything else you’d like to add to the project details? • What’s the top hurdle you’re facing with this project? • What is the timeline to get this done? Why Choose Me? 250+ Projects. 5 Years. Zero Misses. My reputation is built on a single metric: Flawless Execution. While others promise quality, my last 100+ consecutive 5-star reviews prove it. I don’t just finish the job; I set the standard. The portfolio here is just the tip of the iceberg. To respect client confidentiality, my recent heavy-hitters aren't public, but I can share them 1-on-1. Regards, Ali .

@rbtech1984

Hello!! The solution will be a scalable, compliance-focused monitoring platform that discovers and classifies relevant trading audiences, processes large volumes of public data, removes duplicates, and delivers real-time keyword alerts. * Which platforms are the highest priority for the first phase? * Do you already have approved API access for any platforms? * Which keywords and trading topics should trigger alerts? A distributed architecture can handle crawling, ingestion, classification, deduplication, search, alerts, and large-scale storage while keeping each data source modular for future expansion. Platform rate limits, API restrictions, privacy requirements, and terms of service will be respected rather than relying on unreliable workarounds. Relevant large-scale data pipelines, monitoring systems, search infrastructure, and automation projects have been developed with scalability and reliability in mind. The goal is a system that can grow toward millions of records without becoming difficult to maintain. Let us discuss the priority sources and architecture for the first phase. Best regards Farhin B

@gianmput15

I'll build you a large-scale discovery and monitoring system using a distributed architecture with PySpark workers and managed proxy pools, capable of discovering 10M+ trading-interested users across X, Reddit, LinkedIn, YouTube, TikTok, Instagram, Facebook, Discord, Telegram, forums, and blogs—handling API restrictions and rate limits natively with exponential backoff and session reuse. I'll implement a continuous monitoring engine with real-time keyword alerts, cross-platform identity resolution linking the same person across platforms, duplicate detection, and a dashboard for managing accounts, keywords, and alerts. I'll deliver the full architecture with PostgreSQL at scale, deployment guide, and working MVP demonstrating discovery, monitoring, alerting, and user management.