I am looking for an experienced Python developer or algorithm specialist to rigorously test and validate a few scripts that I have. The script locates stocks that have been trending for 2-3 days on the MACD zero line PRE BREAKOUT . Your primary goal will be to stress test the script, identify edge cases, very output accuracy, and ensure robust performance under various conditions. Code review: Evaluate the existing code base for efficiency, security, and best practices. Functional Testing: run the script against sample data sets to verify output, accuracy Edge case testing: intentionally push the algorithm to its limits to find potential bogs, bottlenecks or failure points. Documentation: provide the detailed report of your findings, including reproduction steps for any bogs and recommendations for optimization. Requirements: proven experience in algorithm, testing the bargaining and performance optimization. Strong proficiency in python and relevant testing frameworks. Familiarity with API’s or database Excellent and analytical skills and attention to detail. To apply: please submit a brief proposal, including: 1. Examples of past projects where you tested de BAIRD or optimize an algorithm. 2. Your preferred mythology for testing this type of script.. 3. Your estimated turnaround time for this project..

We are looking for a hands-on senior Python/FastAPI trading-system developer with strong testing, debugging, and broker API experience. This person should be able to work inside an existing codebase, identify what is breaking, fix the issues, and prove the fixes with tests, logs, API output, and dashboard evidence. Ideal experience: • Python backend development • FastAPI • MongoDB • Heroku logs and deployment workflow • React/TypeScript frontend debugging • Alpaca API or similar broker API experience • Trading bot lifecycle experience • Automated testing and regression testing • Market data, candle/bar data, indicators, and timestamp handling • Ability to trace scanner → scoring → decision → order readiness → position state → sell/hold logic This is not a rebuild project. The goal is to test and repair the current system.

Project Overview: I am seeking an expert Python developer to build a lightweight, local automation script that bridges custom TradingView indicator alerts to Interactive Brokers (IBKR) for automated options execution. Core Deliverables: Webhook Listener: A local Python script that securely listens for JSON payloads sent via TradingView webhooks. Dynamic Option Chain Scanner: Upon receiving a payload containing specific parameters (Ticker, Target Delta, DTE), the script must query the live IBKR option chain (using ib_insync or ib_async), find the exact or closest available strikes matching that Delta, and calculate secondary strikes based on distance to the current spot price. Complex Multi-Leg Order Routing: The script must dynamically assemble these strikes into a specific 5-leg complex combo order (combining a credit vertical spread, two debit vertical spreads, and a naked wing) and route it to the market as a single transaction using mid-price algorithmic fills. Rigorous Error Handling & Logging: The code must include strict safeguards (rate limits, bid/ask spread checks, and fallback logic if an exact strike or delta is unavailable) to ensure no rogue orders are fired. Developer Requirements: Demonstrated expertise with the Interactive Brokers API (ib_insync framework heavily preferred) and TWS/IB Gateway. Deep understanding of options mechanics, including how to handle complex multi-leg combinations and delta sourcing. Excellent, proactive communication. Weekly availability during standard New York market hours (EST) for live testing. Must be located in the United States (required for IP protection and NDA enforcement). Workflow & Milestones: Initial development and testing will be done exclusively using an IBKR Paper Trading account. The project will be managed via two strict milestones: 1) Full execution validation in a paper environment, and 2) Live market deployment verification using a single test contract. If you have read this entire post and have experience with IBKR options routing, please start your proposal with the word "DELTA" and briefly describe a past algorithmic trading project you have built.

About Us We are a forward-thinking AI enterprise software company building governance solutions. Our systems combine Python engineering, Natural Language Processing, and Machine Learning to deliver secure governance solutions. We’re seeking a Back-End Python Engineer with expertise in AWS deployed applications, GITHUB CI/CD pipelines, DJANGO, ML Pipelines, Endpoint Integration, Sagemaker, containerization, Use of AI to design front end applications and debug code. Key Responsibilities Design, develop, and maintain back-end services in Python to support software application Debug Application for Quality and Assurance Build Data Connectors for Application Integration Implement new features with front end design as needed Containerize and deploy services across AWS infrastructure. Build and scale RESTful APIs and microservices (Django + DRF) that integrate into automated pipelines. Tune system performance (network, I/O, memory, GPU utilization) for optimization. Architect and maintain databases (SQL & NoSQL), ensuring query optimization, high availability, and caching (Redis). Integrate background processing (Celery) and real-time communication (WebSockets) into containerized environments. Collaborate with DevOps, front-end, and AI/ML teams to deliver end-to-end automated workflows. Apply best practices in system design (SOLID, DRY, KISS), Python standards (PEP8), and secure infrastructure deployment. Qualifications Core Skills Proficiency in Python (OOP, async, functional programming, data structures). Expert-level knowledge of AWS Infrastructure (deployment, operators, CI/CD, scaling). Strong background in containerization (Docker, Podman) and Kubernetes-native orchestration patterns. Experience supporting AI Dev automation workflows and integrating back-end services with automated pipelines. Deep knowledge of Django & DRF: ORM, serializers, view sets, permissions, HTTP methods. Advanced database design & optimization for high-throughput applications. Familiarity with Redis caching, Celery task queues, and uWSGI/ASGI communication layers. Solid testing skills (pytest/unittest) and CI/CD pipelines with Git. Preferred Expertise Hands-on experience with GPU-enabled workloads and hardware acceleration in containerized environments. Familiarity with infrastructure automation tools (Ansible, Terraform, or similar). Agile/Scrum team experience and use of task tracking (Jira, Trello). What We’re Looking For We want an engineer who: PRIORITIZES SECURITY OF SYSTEMS AND INFRASTRUCTURE ACROSS SECURITY FRAMEWORKS Builds automation-first systems that support AI Dev workflows from code to deployment. Thinks about performance and scalability at the infrastructure + software level. Collaborates across teams (DevOps, AI/ML, product) to deliver fully integrated, automated platforms.

We are building a production SaaS platform called MarketLens, designed for investment teams and analysts who need real-time market intelligence combined with AI-generated insights and portfolio tracking tools. What You’ll Do - Build and maintain the core backend services in Python (FastAPI / Django) - Develop frontend dashboards using React (TypeScript preferred) - Integrate real-time data pipelines (WebSockets, streaming APIs, or message queues) 1. Implement AI features such as: 2. Market summarization using LLMs 3. Portfolio risk explanations 4. Automated insight generation from time-series data - Design and maintain scalable APIs for analytics and user data - Work on subscription and billing logic (Stripe integration) - Improve system performance, especially around data freshness and dashboard latency - Participate in architecture decisions for scaling AI + data workloads Our clients are small to mid-sized hedge funds and financial advisory teams who currently rely on fragmented tools like Bloomberg exports, spreadsheets, and separate analytics dashboards. MarketLens aims to unify all of this into a single workflow.

This is what I am looking for: Long Beach, CA - all dispensary info ~10 fields to fill-in Los Ángeles, CA - all dispensary info ~10 fields to fill-in Please let me know how you would approach this within your tech stack. I also need a quick turn around, so let me know how long it would take you to complete? There should be about 200 records in total. Ive attached a copy of my table for your review. Thank you!

We sell banknotes, coins, and postcards. We image thousands daily as we sell thousands daily. We currently write our own 40-60 word descriptions of these items using a CSV spreadsheet. We would like AI to write the descriptions! Looking for a widget/software/app/whatever that is PC based-has the ability for us to drag a folder of 1000 images into it and it spits out a spreadsheet of 2 columns. SKU (get into that later) and 40-60 word description. Example: 1952 France 5 Francs banknote or 1964 Kennedy Half Dollar by AI viewing the images of these items. A picture postcard of "visit Virginia" when viewed by AI will be Girl in white hat says visit Virginia" I'm told by a few folks it needs to have scripts to run it....that's not by forte. Yours is hopefully!

Here’s what I’m doing: I’m looking to make our lead research process better and get it fully automated. Here’s the process as it stands now: 1. We start with a list of companies and their websites. 2. For each one: scrape their site for shipping facility locations — warehouses, DCs, manufacturing plants. Check their locations/facilities pages and their careers page. Job postings for warehouse, forklift, shipping & receiving, production roles confirm an active facility and usually give the address. Careers pages are inconsistent — some are static HTML, some run through Workday, Greenhouse, iCIMS, or other ATS platforms that don’t always let scrapers in. I need someone who’s dealt with that before and knows how to handle it, not just scrape the easy ones and skip the rest. 3. Identify contacts matching these titles: Transportation Manager, Director of Transportation, Logistics Manager, Director of Logistics, Traffic Manager, Senior Transportation Manager, Senior Logistics Manager, Logistics Sourcing Manager, Logistics Procurement Manager, Transportation Procurement Manager, Transportation Sourcing Manager. 4. Score every contact for whether they’re actually still there — not just whether they show up in a database. Apollo and ZoomInfo are full of people who left or retired years ago but still show as active. The scorecard has to catch that before it goes any further. 5. Enrich the contacts that pass: email and phone. Phones: enriched numbers are usually garbage with no way to verify them. Scrape the company website for the corporate number instead. No 800 numbers — those are dead-end customer service lines. I want a local number so I can call and ask whether the person is still there. I know distinguishing a corporate number from an 800 number on a scraped page isn’t always straightforward — some sites only list the 800 number, some bury the local number in a footer or contact page. Tell me how you’d approach that. Emails: fine as-is — enrich, run through NeverBounce. Non-negotiable: every contact comes out with a score and the specific reason behind it. No black box. I need to see why someone scored high and why someone else scored low. End state: I drop in the list, it runs, output comes back scored and enriched with facility locations attached. Fully automated, repeatable.

We are seeking a highly skilled Senior Software Engineer with extensive experience in Python development. The ideal candidate should possess not only technical expertise but also excellent communication skills to collaborate effectively with cross-functional teams. Your role will involve designing, developing, and maintaining robust software solutions while ensuring clarity in technical discussions. If you are passionate about coding and thrive in a dynamic environment, we want to hear from you!

I am looking for a automation to be built that will trigger when new PDFs are uploaded into a google drive. The automation would scan each new PDF, extract certain information, and place that information on a google sheet. The PDFs are scanned images of documents off a computer screen (public notices) and the format has some variability to it, the PDFs are not always in the same format. I would like it to be scalable so I can easily add new document types later.