Freelancers using Python in Chennai

Freelancers using Python in ChennaiFreelancers using Python in Chennai

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Algo Trading Developer - Pine Script,MQL5 &Python Automation

34: Followers

Algo Trading Developer - Pine Script,MQL5 &Python Automation

Cover image for Designed and implemented a robust

Designed and implemented a robust webhook-based trading automation system that connects TradingView signals to real-time trade execution via the Tradovate API. The system is built using Python and handles signal reception, validation, and order execution in a seamless automated pipeline. The architecture ensures low-latency communication, secure data handling, and reliable execution of trades without manual intervention. It processes incoming webhook alerts, applies validation logic, and triggers precise API-based trade execution. Key features include: • Real-time webhook signal processing (TradingView alerts) • Python-based signal receiver and validation engine • Secure and reliable API integration (Tradovate) • Low-latency execution pipeline • Fully automated trade workflow (zero manual input) • Scalable design for multi-strategy integration This solution is ideal for traders and firms looking to automate their strategies using webhook-driven architecture, enabling faster execution, reduced human error, and consistent performance in live trading environments.

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Cover image for Developed an automated options trading

Developed an automated options trading bot using Python that executes trades on Deriv based on real-time strategy signals and multi-condition validation logic. The system is designed to operate continuously with minimal latency, ensuring fast and accurate trade execution in volatile market conditions. The bot integrates signal generation, validation, and execution into a single automated workflow. It evaluates multiple technical indicators, applies strict entry conditions, and triggers trades only when high-probability setups are detected. Key features include: • Fully automated options trading (Deriv API integration) • Real-time signal processing and execution • Multi-indicator validation logic for higher accuracy • Risk management controls and trade filtering • Continuous operation with minimal manual oversight • Scalable architecture for strategy customization This solution enables traders to automate their options trading strategies end-to-end, improving consistency, speed, and discipline while eliminating emotional decision-making.

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Cover image for Developed a fully automated trading

Developed a fully automated trading system for MetaTrader 5 (MT5) that executes trades based on validated multi-indicator signals. The system integrates TradingView-generated signals with a Python-based processing engine, which evaluates multiple technical conditions before triggering precise trade execution. The Expert Advisor (EA) is designed with advanced trade logic, risk management, and real-time execution capabilities. It ensures consistent performance by removing emotional decision-making and strictly following predefined rules. Key features include: • Automated trade execution on MT5 (buy/sell orders) • Integration with external signal sources (TradingView via webhook) • Multi-indicator validation (RSI, EMA, ADX, Volume, etc.) • Built-in risk management (lot sizing, SL/TP control) • Real-time processing with minimal latency • Fully customizable strategy logic This system enables traders to automate their strategies end-to-end — from signal generation to execution — ensuring speed, accuracy, and consistency in live market conditions.

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Cover image for Engineered a high-performance Python-based trading

Engineered a high-performance Python-based trading signal engine designed to generate precise and reliable trade signals using multi-indicator confluence logic. The system evaluates over 12 technical indicators including RSI, EMA, ADX, Volume, and trend-based conditions to identify strong market opportunities while effectively filtering out noise and false signals. The architecture is built to process real-time market data, validate multiple conditions simultaneously, and trigger only high-probability trade setups. This significantly improves signal accuracy and reduces unnecessary trades. Key capabilities include: • Multi-indicator confluence engine (12+ conditions) • Real-time signal processing and validation • False signal reduction using layered logic • Scalable architecture for integration with trading APIs • Seamless compatibility with automated execution systems (webhooks, APIs) This system can be integrated with platforms like TradingView, Tradovate, or custom trading environments to enable fully automated trading workflows — from signal generation to execution. Designed for traders and firms looking to achieve consistent, rule-based, and emotion-free trading through intelligent automation.

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siva karthik angarayan

I lead a 20-engineer dev team that ships MVPs and SaaS build

New to Contra

I lead a 20-engineer dev team that ships MVPs and SaaS build

Cover image for Dispatch Dudes is a premium

Dispatch Dudes is a premium enclosed auto transport brokerage based in Florida, specializing in moving high-value and exotic vehicles across the US. They connect car owners with vetted carriers, handling everything from instant quoting to coordinating pickup and delivery with white-glove service. With BBB accreditation, Forbes Business Council membership, and insurance coverage up to $450K, they operate at a level of trust and reliability that most brokerages can't match. It's the go-to option for anyone who won't put their vehicle on just any truck.

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Cover image for ConversionOps is AI-powered lead conversion

ConversionOps is AI-powered lead conversion infrastructure built for real estate agents and brokerages who are losing deals to slow follow-up.

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Cover image for Kisthenics AI is an AI-powered

Kisthenics AI is an AI-powered calisthenics coaching platform that builds personalized training programs based on your current skill level and goals. It guides you through progressive bodyweight training from beginner fundamentals all the way to advanced movements like muscle-ups and handstands , adapting as you improve. The platform combines structured programming with real-time feedback so you're never guessing what to train next or whether you're progressing correctly. It's built for people who want the structure of a personal coach without the cost of one.

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Cover image for Vela is an AI-powered executive

Vela is an AI-powered executive communication intelligence platform that sits on top of your email. It analyzes your inbox to surface what actually needs your attention, flags decisions buried in threads, and helps executives spend less time managing email and more time acting on what matters. Think of it as a decision layer for your inbox , not just a smart filter, but a system that understands context, urgency, and relationships across your communication history. It's built for founders and senior executives who are drowning in email but can't afford to miss anything important.

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Building scalable Next.js, Flutter & AI applications

New to Contra

Building scalable Next.js, Flutter & AI applications

Cover image for RAG is only as good

RAG is only as good as the data you feed it. 📄➡️🤖 I am excited to share that I’ve completed the Build an AI-Powered Document Retrieval System with IBM Granite and Docling lab from IBM SkillsBuild! While my previous work focused on the RAG pipeline, this lab went deeper into the most critical step: Document Parsing. We often forget that real-world data isn't clean text—it's locked in complex PDFs and formatted documents. What I built in this hands-on lab: 🔹 Advanced Parsing with Docling: I used Docling to not just "read" text, but to understand the structure of documents, preserving the context for the AI. 🔹 Granite Power: Leveraged IBM Granite models (granite-embedding-30m-english) to create high-quality vector embeddings. 🔹 Seamless Integration: Orchestrated the entire workflow using LangChain to connect the parsed data with the retrieval engine. This skill allows me to build AI agents that don't just "guess" answers but can accurately retrieve information from complex business documents. Technical breakdown of what I built: 🔹 Orchestration: Used LangChain to manage the flow between the user, the database, and the model. 🔹 Embeddings: Leveraged IBM Granite models (granite-embedding-30m-english) to convert text into vector representations. 🔹 Data Processing: Implemented document loading and chunking strategies to optimize context windows. 🔹 Synthesis: Created a system that retrieves relevant data and generates accurate, fact-based summaries. This experience has given me the practical skills to build AI applications that are not just "smart," but also accurate and domain-specific.

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Cover image for VitaCare🚀

1. Immutable Health Records

VitaCare🚀 1. Immutable Health Records (Blockchain & AES-256 Encryption) I moved beyond standard database storage to build a Tamper-Proof Medical Ledger. I learned how to implement a hybrid storage strategy where sensitive patient data is encrypted via AES-256 at the application layer before being anchored to a blockchain. This taught me how to ensure absolute data integrity, making medical histories immutable while providing a verifiable audit trail for every access request. 2. Privacy-First Consent Logic (Granular Data Sharing) Architecting the "Time-Limited Access" protocol taught me how to handle high-stakes privacy. I engineered a system where patients can issue temporary, scoped decryption keys to doctors via smart contracts. This taught me how to implement a Zero-Trust architecture, ensuring that healthcare providers only see what they need, exactly when they need it, with access automatically revoking after a set TTL (Time-To-Live). 3. Edge-Optimized Backend & Secure Validation By leveraging Supabase Edge Functions, I learned how to move critical business logic closer to the user while maintaining a "Thick-Client, Secure-Server" model. I architected isolated server-side environments for data validation and healthcare-specific compliance checks, which taught me how to drastically reduce latency in high-volume environments without compromising on server-side security. 4. Proactive Health Intelligence (Predictive Monitoring) I leveled up my AI integration skills by building an Advanced Command Center for Disease Surveillance. I learned how to aggregate anonymized, real-time data from disparate sources—including IoT wearable integrations—to generate heatmaps for disease outbreaks. This taught me the complexity of Geospatial Data Engineering and how to turn passive monitoring into proactive healthcare interventions. 5. Multi-Platform Synchronization (Unified Digital Ecosystem) Building a system that bridges Citizens, Doctors, and Government officials taught me the challenges of Cross-Stakeholder State Management. I learned how to maintain a "Single Source of Truth" across a multilingual Next.js web ecosystem and mobile interfaces, ensuring that a life-saving update on a doctor's portal is reflected on a patient's mobile dashboard in near real-time. 6. Inclusive Design & Localized Accessibility To tackle the diversity of the Indian healthcare landscape, I implemented a Multilingual UI Framework. I learned how to architect a scalable localization layer that supports regional languages, ensuring that the platform is accessible to rural citizens. This taught me the importance of Inclusive UX Engineering—where the technical complexity is hidden behind a simple, high-impact interface for non-technical users.

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Cover image for MIT Connect🎉

1. Hierarchical Access

MIT Connect🎉 1. Hierarchical Access Control & Multi-Tenant Architecture I moved beyond basic authentication to implement a granular Role-Based Access Control (RBAC) system. By architecting a "Portal-Switch" logic, I learned how to serve distinct frontend environments (Admin vs. Student) from a unified backend, ensuring that administrative actions like fee management and academic overrides are cryptographically isolated from student-level access. 2. Predictive Academic Logic & Real-time Analytics Instead of static data display, I engineered a Proactive Attendance Engine. I learned how to write complex backend aggregation pipelines that don't just calculate percentages, but run "Safe-Miss" simulations. This taught me how to transform raw timestamped logs into actionable insights, helping users predict eligibility before it becomes a critical failure point. 3. Optimized Grid Scheduling & Sparse Data Handling Building the Dynamic Timetable Matrix taught me how to manage high-density relational data with significant "empty states." I learned how to optimize frontend rendering for a 2D coordinate-based schedule (Time vs. Day), ensuring that the UI remains performant and responsive even when mapping hundreds of unique course-section combinations across a decentralized database. 4. Financial Integrity & Transactional Consistency Handling the Invoices and Fee Administration module taught me the importance of ACID compliance. I learned how to architect transactional workflows in the database to ensure that financial records—from generation to payment status—remain immutable and consistent, preventing data drift in multi-step billing cycles. 5. Component-Driven Design & Scalable UI Systems To maintain consistency across the Analytics and Academic modules, I developed a proprietary library of reusable UI components. I learned how to build "Data-Agnostic" widgets—such as the Stat Cards and the Weekly Trend Bar Charts—that can be hot-swapped across different dashboards, drastically reducing technical debt and ensuring a uniform brand identity. 6. High-Throughput State Management Building the Intelligence & Analytics suite taught me how to manage global state across a complex dashboard ecosystem. I learned how to implement optimized fetching strategies (like SWR or React Query) to ensure that when an Admin updates an event or a student marks an attendance hour, the change propagates across the entire system without requiring manual refreshes or redundant API overhead.

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Cover image for Cognitive Guardian: Building Cognitive Guardian

Cognitive Guardian: Building Cognitive Guardian was a massive undertaking that pushed me to bridge the gap between physical hardware and cloud infrastructure. Transitioning this from a conceptual idea to a fully integrated digital tether taught me invaluable lessons in full-stack architecture, IoT, and edge computing. Architecting Resilient Systems (Failover Logic): I learned how to design a system that doesn't just fail gracefully, but adapts. Building the "Offline Handshake" protocol taught me how to seamlessly hand over session logic from a smartphone (Cellular/BLE) to a microcontroller (LoRaWAN) when entering network dead zones. Edge AI & Hardware-Software Integration: Instead of relying on cloud-based machine learning (which introduces latency), I learned how to program Edge AI natively on a microcontroller. Writing C++ state machines to calculate 3D acceleration vector magnitudes (via an MPU6050 sensor) taught me how to achieve zero-latency anomaly detection while operating under strict hardware constraints. Polyglot Database Strategy: I leveled up my data engineering skills by realizing one database doesn't fit all. I learned how to route high-throughput, real-time GPS telemetry into MongoDB (leveraging 2dsphere indexes for geospatial queries), while using PostgreSQL for strict relational state tracking, and Hyperledger Fabric for immutable audit logs. Privacy by Design (Self-Sovereign Identity): Handling sensitive medical data taught me modern compliance and security. I learned how to implement Decentralized Identifiers (DIDs) on a permissioned blockchain, ensuring that user data remains encrypted and is only temporarily accessible to authorities via smart contracts during an active SOS. Mobile Battery Optimization & Background Tasks: On the Flutter side, I learned how to handle intensive background processes without killing the user's device. Implementing dynamic location polling tied to the phone's internal accelerometer taught me deep, native-level power optimization for both Android and iOS. Managing High-Velocity Data Streams: Building the Node.js/Express backend taught me how to handle asynchronous data spikes. I learned to implement rate-limiting and use Socket.IO (http://Socket.IO) to bypass standard HTTP request cycles, successfully pushing critical hardware SOS alerts to a React web dashboard in under two seconds.

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Aditya Mohapatra

ML & Data Science | IIT Madras Graduate

ML & Data Science | IIT Madras Graduate

Cover image for Random walk scaling on networks

Random walk scaling on networks

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Cover image for Waste Segregation CNN

Waste Segregation CNN

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Cover image for Web Scrapping and NLP

Web Scrapping and NLP

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Thananjayan Rajasekaran

Expert QA Engineer | Automation & Test Ops Guru

Expert QA Engineer | Automation & Test Ops Guru

Cover image for Test Scripting to Monitoring

Test Scripting to Monitoring

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Cover image for k6: Live streaming of results and monitoring

k6: Live streaming of results and monitoring

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Cover image for Java-Based Testing Frameworks for Software Quality Assurance

Java-Based Testing Frameworks for Software Quality Assurance

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Fullstack Developer, Certified DevOps Engineer

Fullstack Developer, Certified DevOps Engineer

Cover image for ohitsmeMohit/Song-recommender-System

ohitsmeMohit/Song-recommender-System

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Cover image for Portfolio Website

Portfolio Website

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Cover image for KimtVak8143/ClimateActionPlanner

KimtVak8143/ClimateActionPlanner

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Senior Software Engineer

Senior Software Engineer

Cover image for Power up your customer support with Zendesk

Power up your customer support with Zendesk

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Cover image for Signet Jewelers - Signet Jewelers | Home

Signet Jewelers - Signet Jewelers | Home

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Cover image for IDFC FIRST BANK

IDFC FIRST BANK

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Pixel-Perfect Fullstack Engineer

Pixel-Perfect Fullstack Engineer

Cover image for Faculty Leave Management System

Faculty Leave Management System

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Cover image for DebugMaster: A Competitive Debugging Event Platform

DebugMaster: A Competitive Debugging Event Platform

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Cover image for Human Anatomy AR VR

Human Anatomy AR VR

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