AI Trading Back-Office System

@Guptapuru304

Hello, We've reviewed your project for developing an AI Trading Back-Office System, and it aligns perfectly with our expertise. Your need for a comprehensive platform that integrates database engineering and AI for seamless transaction processing and insightful data analysis is well understood. Previously, we developed a scalable trading analytics platform that delivered real-time risk management and performance reporting. This experience enables us to provide a tailored solution for your back-office needs. With over 8 years in AI-first product development, our team, led by Puru Gupta, excels in creating systems that are intelligent, scalable, and compliant. Our proficiency in Python, FastAPI, PostgreSQL, and AI model development ensures robust database architecture, transaction processing, and insightful analytics. Our portfolio includes top-rated solutions on Freelancer.com, showcasing our commitment to high-quality, customer-centric delivery. We are confident in building a platform that exceeds your expectations. Please message us with more details, and we will provide a comprehensive proposal within 24 hours. Best regards, Puru Gupta and Team

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Smart Back-Office Platform for Indian Markets ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for an end-to-end back-office platform for Indian markets. You don’t need to look any further; Zohaib is here to help you! My team has already completed 50+ similar projects for trading platforms. I will create a robust database architecture, build efficient transaction workflows, and design AI models to enhance performance and compliance. ➡️ Why Me? I can easily build your back-office platform as I have 5 years of experience in database engineering and AI integration. My expertise includes transaction processing, data analysis, and risk management. Besides, I have a strong grip on REST APIs and analytics dashboards, ensuring a smooth workflow for your trading desk. ➡️ Let's have a quick chat to discuss your project details. I can show you samples of my previous work that highlight my capabilities. Looking forward to chatting with you! ➡️ Skills & Experience: ✅ Database Architecture ✅ Transaction Processing ✅ Data Analysis ✅ AI Model Design ✅ Risk Management ✅ Analytics Dashboard ✅ REST API Development ✅ Workflow Automation ✅ Performance Reporting ✅ Historical Data Analysis ✅ Compliance Tools Integration ✅ Real-Time Data Processing Waiting for your response! Best Regards, Zohaib

@LiveExperts

Hi, This is a strong and well defined system and I can help design and build it end to end with a focus on speed reliability and auditability. I have experience building data platforms using Python FastAPI PostgreSQL analytics pipelines and AI driven reporting. For your case I would structure the system around an event driven backend where every trade position change and cash movement is captured with full versioning and audit trace. The database layer can be built on PostgreSQL or TimescaleDB with optimized ingestion to meet the 10k writes per second target. Trade processing workflows will be designed for straight through handling including capture allocation settlement and reconciliation while keeping error rates low. For the AI layer I would focus on practical risk monitoring. This includes detecting exposure breaches unusual activity reconciliation gaps and market driven risks. Alerts can run continuously and generate clear summaries for end of day reporting. The dashboard will allow filtering by strategy symbol trader and time range with fast rendering and export to PDF or Excel. Clean APIs will be exposed for integration with other tools. You will receive source code Docker setup deployment steps API documentation and a user guide. Happy to review your data structure and start quickly.

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you need a back-office trading platform for NSE, BSE, and MCX with near real-time trade processing, audit trails, reconciliations, AI risk insights, analytics dashboards, and clean APIs. I’ve worked on data-heavy platforms with transaction workflows, reporting, role-based dashboards, and scalable database design. My approach would be to first design the trade/position/cash ledger model, then build ingestion + reconciliation workflows, followed by AI risk alerts, historical reporting, and API layers for compliance/front-office tools. I’d be happy to review the full acceptance criteria and suggest a realistic architecture, timeline, and milestones. I have a few questions to get a better understanding: Q1 – What systems or broker APIs will trades and market data come from? Q2 – Do you need SEBI/compliance-specific reporting formats included from day one? Q3 – Should the AI risk layer be rules-based first, or do you already have historical data for model training? Looking forward to hearing from you.

@Mahihassi

As a seasoned AI and Cloud Developer, I have built several scalable backend systems and AI-powered platforms fused with intuitive web dashboards. I am highly proficient in Python, FastAPI, Node.js, React, and relevant database systems that align seamlessly with your preferred stack. My primary goal is to create intelligent applications that combine AI models, cloud infrastructure, and user-friendly dashboards - precisely what you need for your AI Trading Back-Office System. While being a whiz in AI model development, I am also experienced in crafting robust transaction-processing workflows equipped with full audit trails - a vital module required for your system. My work style encompasses clean architecture, scalability, and production-ready systems – qualities that align perfectly with your project. In summary, I offer a unique blend of skills including backend architecture design, REST APIs and microservices development, AI integrations and automation tools, as well as data analytics and visualization systems. By choosing me, you'll be working with someone dedicated to excellence in every aspect of the job; from avoiding manual intervention for your front-end and compliance tools through REST or gRPC APIs to ensuring acceptance criteria are not just met but exceeded. Partnering with me means receiving an end-to-end service that will transform your trading desk operations - helping you move faster while maintaining compliance!

@irsolutions

With over a decade of experience in full-stack architecture and high-scale systems, I understand your need for an AI Trading Back-Office System that integrates cutting-edge technology to drive efficiency and compliance for your trading desk. My background in building and scaling complex systems, such as the Telegram Mini Apps serving over 1 million users, uniquely positions me to tackle the challenges of your project head-on. For your AI trading platform, a strategic insight I would recommend is leveraging Docker/Kubernetes for seamless deployment and scalability. With a track record of handling high-volume transaction processing and data analysis, I am confident in delivering a robust database architecture and AI layer that meet your performance benchmarks. I invite you to reach out to discuss how I can contribute to the success of your project. Let's collaborate to create a bespoke solution tailored to your specific needs.

@corpmember29

I can build your end-to-end trading back-office system with real-time trade processing, audit-ready database architecture, and AI-driven risk analytics. Using Python (FastAPI), PostgreSQL/TimescaleDB, and streaming workflows, I’ll ensure sub-2s trade ingestion, 10k writes/sec scalability, and fast historical reporting under 5 seconds. The system will include automated trade capture, reconciliation, full audit trails, and an AI layer for risk alerts and EOD performance summaries. A clean API layer (REST/gRPC) and dashboard (Grafana/Streamlit) will support traders and compliance teams in real time. Let’s connect and I’ll outline a clear architecture and execution plan.

@kamran2012

Hi, I will build your trading back-office platform — trade capture and STP workflows, AI-driven risk monitoring, historical performance analytics, and REST/gRPC APIs — all containerized and deployment-ready. For the database layer, I will use TimescaleDB with hypertables partitioned by trade date, which handles the 10k writes/second target while keeping one-year analytical queries fast through continuous aggregates. The risk engine will run as an async service scanning positions against configurable limit thresholds every 30–60 seconds, pushing alerts via WebSocket so your desk sees breaches instantly — not after a page refresh. Questions: 1) What market data feeds will the risk engine consume — do you have an existing provider with an API, or should I factor in integration with a specific vendor? Looking forward to your response. Best regards, Kamran

@DeveloperStation

Hi there, We’ve built similar back-office systems for trading firms, where we integrated with multiple brokers and developed workflows for trade capture, allocations, settlements, and reconciliations. We also designed AI models to flag risk exposures and generate end-of-day summaries. For your project, we can leverage our expertise in Python, FastAPI, and Azure/AWS to create a robust, scalable solution. We’re committed to delivering a product that meets your needs and exceeds expectations. Let’s schedule a 10-minute introductory call to discuss your project in more detail and see if I’m the right fit for your needs. Best, Adil

@Feriver

Hello, I will design and build a high-performance AI-powered trading back-office platform with a strong focus on real-time data integrity, auditability, and risk intelligence. The system will use a scalable architecture built on FastAPI with a high-throughput data layer powered by PostgreSQL (optionally extended with TimescaleDB for time-series optimization). All trade events will be processed through structured pipelines ensuring full audit trails, near real-time ingestion, and strict consistency. AI modules will be developed using scikit-learn (and expandable to TensorFlow where needed) to monitor positions, detect anomalies, and generate continuous risk insights. These outputs will feed a reporting engine that produces end-of-day summaries and compliance-ready analytics. A real-time dashboard will be implemented for performance slicing (strategy, symbol, trader, time range), with export capabilities to PDF/Excel. The system will also expose secure REST APIs for seamless integration with front-office and compliance tools, fully containerized using Docker and designed for horizontal scaling. Thanks, Asif.

@Infineosoft

Hi We can design and deliver a secure, end-to-end back-office platform tailored for your trading desk, combining high-performance data engineering with practical AI-driven insights. The system will be built on a scalable, event-driven architecture to ingest and version trades, positions, and cash movements in near real time. I’ll implement straight-through processing workflows covering trade capture, allocations, settlements, and reconciliations, all backed by immutable audit trails to ensure full compliance and traceability. On top of this, I’ll integrate an AI layer that continuously monitors positions and market data to detect anomalies, risk exposures, and exceptions during the trading day. These insights will be compiled into clear end-of-day reports with actionable summaries for faster decision-making. You’ll also get a powerful analytics dashboard with flexible filters (strategy, symbol, trader, time range) and one-click export to PDF or Excel. Clean REST/gRPC APIs will be exposed for seamless integration with front-office and compliance systems. The platform will meet strict performance targets, including sub-2-second trade processing and high data accuracy. Security, scalability, and reliability will be built in from day one. Happy to discuss your current stack, data sources, and compliance requirements to refine this further.

@yuliussmayoru

OVER 10 YEARS OF EXPERIENCE IN IT, HIGH QUALITY DELIVERY Hey there! Trading systems often struggle with slow processing, broken reconciliation, and poor real-time risk visibility — I’d tackle this with a fast event-driven architecture, clean audit trails, and an AI layer that actively monitors exposures as trades flow in. I’ve spent over 10 years building high-performance backend and financial data systems, so handling near real-time trade ingestion, settlements, and compliance tracking is something I’m very comfortable with. What excites me here is the balance between reliability and intelligence — I’d design a scalable database with versioned records and immutable logs, while exposing clean REST/gRPC APIs so your front office and compliance tools stay perfectly in sync. On the AI side, I focus on practical value: models that flag anomalies, risk exposure, and mismatches during the day, then generate clear end-of-day reports your team can actually use. I also build dashboards that are fast and genuinely useful — letting you slice performance by trader, strategy, or symbol and export instantly. I work independently, communicate clearly, and take full ownership, so you’ll get a system that’s not just powerful, but dependable and easy to operate every day.

@webmasters486

Hey, I’ve reviewed your project and understand you need a robust back office platform that combines high performance transaction processing with AI driven analytics and risk monitoring. The goal is to create a scalable system that ingests trades in near real time, maintains full auditability, and delivers actionable insights for faster and compliant decision making. I will design a scalable architecture using Python and FastAPI with PostgreSQL or TimescaleDB to handle high volume trade data with versioning and audit trails. Transaction workflows will cover capture, allocation, settlement, and reconciliation with sub second processing targets. On top of this, I’ll implement AI driven analysis using structured pipelines to detect risk exposure, anomalies, and generate continuous alerts and end of day summaries. A dashboard will allow slicing performance by strategy, symbol, and time range with export options, while clean APIs will expose data for external systems. Before delivery, I will validate performance benchmarks including high throughput writes, fast reporting, and reliable alert generation. You’ll receive containerized deployment, full source code, and clear documentation for operation and scaling. Let’s connect so I can align on your data structure and execution flow. Best regards, Muhammad Adil Portfolio: https://www.freelancer.com/u/webmasters486

@SmartCodeDev

Hey, this is my kind of build, heavy data + real-time constraints. I’d design this around a high-throughput event pipeline: ingestion via async services in FastAPI, streaming through Kafka, and storage in TimescaleDB so we can handle tick-level data + versioning cleanly. That setup easily supports 10k+ writes/sec with proper partitioning and batching. Trade workflows (capture → allocation → settlement → reconciliation) would be event-driven with full audit logs, so every state change is traceable. For the AI layer, I’d start with real-time rule + statistical models (exposure thresholds, anomaly detection), then layer ML using scikit-learn once patterns stabilize — alerts pushed every minute or faster if needed. Analytics side: pre-aggregations + indexed queries so 1-year reports render fast, exposed via APIs (REST/gRPC) and visualized in something like Grafana or Streamlit depending on how interactive you want it. Everything containerized with Docker/K8s, CI/CD included, plus load testing + benchmarks to prove latency and throughput targets. Can break this into phases and get a solid MVP running quickly, then iterate on models and dashboards

@Aaleen110

Having developed high-concurrency data pipelines for quantitative desks, I recognize that the bridge between trading execution and AI insights requires impeccable integrity and speed. In a recent engagement, I engineered a trade reconciliation engine processing millions of daily transactions, integrating a custom LLM layer to identify execution anomalies traditional systems missed. I understand your back-office must be a high-performance foundation that translates complex activity into actionable intelligence while maintaining a strict audit trail. My technical approach focuses on building a modular architecture using Python and a high-performance database layer, specifically TimescaleDB for time-series logs and PostgreSQL for relational metadata. I will implement an asynchronous processing pipeline utilizing Celery to handle data ingestion from your execution gateways without bottlenecking core logic. The AI component will be integrated as a microservice, utilizing LangChain for automated report generation and vector embeddings for natural language querying of performance data. For the UI, I recommend a React dashboard with WebSockets to provide real-time telemetry on system health and AI-detected risk signals. To tailor the database schema, are you pulling data from exchange APIs or a FIX engine, and what is the expected peak transaction volume? Do you have a preference for AWS or GCP for infrastructure? I would appreciate a brief chat to discuss the AI models you intend to deploy and how we can optimize the feedback loop; let me know when you are ready to align on the technical roadmap.

@mugencodes

Hello, I understand the importance of having a seamless AI trading back-office system that can enhance efficiency and compliance for your trading desk. With expertise in Python, AI model development, database management, and machine learning, I am well-equipped to tackle the scope of work outlined in your project description. My approach involves building a scalable database architecture that can handle real-time data processing and versioning, creating robust transaction-processing workflows, designing AI models for risk analysis, and developing an analytics dashboard for performance tracking. I will ensure that all trades are processed swiftly, historical performance reports are generated promptly, risk alerts are timely, and the database can handle high loads without data loss. I am confident in my ability to deliver a solution that meets your acceptance criteria and exceeds your expectations. Let's work together to create a cutting-edge back-office platform that empowers your trading operations. Best regards, Jayabrata Bhaduri

@vladang

The real risk here isn’t building features—it’s maintaining sub-second consistency between high-frequency writes, auditability, and AI-driven risk signals without introducing data lag or reconciliation drift. I’d structure this around an event-driven pipeline: trade events ingested via FastAPI → queued (Kafka/Redis Streams) → written to TimescaleDB with append-only + versioned records for full auditability. Transaction workflows (capture → allocation → settlement → reconciliation) run as idempotent services to guarantee <0.1% error rates. For performance, partitioned hypertables and write batching will sustain 10k writes/sec. The AI layer would use streaming features (rolling exposures, VaR approximations) with lightweight models in scikit-learn for real-time alerts, and Pandas-based batch jobs for end-of-day reports. I’d also add pre-aggregated materialized views to ensure <5s reporting latency. I’ve built similar systems in Aras (Python APIs handling high-volume financial data) and Bubbl (scalable API architecture under heavy concurrent load). Happy to map this to your desk’s exact workflows. Q1: What’s the expected trade/event schema variability across instruments? Q2: Do risk models need explainability for compliance audits? Q3: How strict is consistency between real-time and end-of-day data snapshots?

@markupdudes

Hi, Your project to build an AI-driven trading back-office system tailored for NSE, BSE, and MCX markets is compelling and precisely aligned with my expertise. With extensive experience in database architecture, AI model development, and real-time financial transaction processing, I can help you create a scalable platform that ensures compliance while boosting trading efficiency. I will design a near real-time database to handle every trade, position, and cash flow with version control. The AI layer will provide continuous risk management through smart alerting and detailed performance reports. Additionally, the analytics dashboard with export capabilities and robust APIs will enable seamless integration with your front-office and compliance tools. I propose we start with requirements finalization and database schema design within the first 10 days, followed by iterative development and testing to meet your 2-second trade processing criteria. Could you share more about the expected daily trading volume and peak loads to better tailor the system's scalability requirements? Best regards,

@Eliteinno

Hi, My team and I just reviewed your project, and it seems like the core challenge lies in crafting an Architect that can Scale efficiently to handle high-frequency trading data. Our backend leads excel at creating robust database frameworks that ensure real-time processing and compliance. We recently completed a large-scale deployment for a financial firm using Python, PostgreSQL, and Docker. The system effortlessly managed over 15k transactions per second, providing near-instantaneous risk assessments and comprehensive reporting. I'll be your direct technical point of contact, ensuring a seamless integration of your AI models with the database. We’ll establish a dedicated staging environment to fine-tune each component before it goes live. How do you envision this platform evolving to support future trading strategies? Let's explore how our expertise can align with your goals.