Freelance python jobs for world-class Developers

Job Title
High-Frequency Trading Bot Developer – Rithmic Time & Sales (Order Flow) – MT5 Execution

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Job Description

I am looking for a serious and experienced algorithmic trading developer to build a high-frequency scalping bot based primarily on Time & Sales (executed trades) using Rithmic data, with execution on MetaTrader 5 (IC Markets – US500).

This is not an indicator-based bot, not a grid, not martingale, and not a demo/UI-only project.
I need a real working trading system, inspired by professional order-flow / HFT concepts, adapted to a retail execution environment.

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## Project Objective

Build a second trading bot focused on Time & Sales order flow, capable of generating approximately 500–800 trades per day (600+ target) during liquid market hours.

The bot must:
- Analyze executed trades only (Time & Sales)
- Detect aggressive trade bursts
- Trade micro price movements (1–2 ticks)
- Exit very fast (seconds)
- Respect IC Markets rules

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## Data Source (Mandatory)

- Rithmic Time & Sales (Last Trade feed)
- Each trade includes:
- timestamp (milliseconds)
- price
- volume
- aggressor side (BUY at ask / SELL at bid)

DOM Level 2 is optional, not required.

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## Strategy Logic (Must Be Implemented)

### 1) Market Context (Average Flow)

Continuously compute a rolling average over the last 3–5 seconds:
- trades per second
- BUY vs SELL balance
- average delta (BUY – SELL)

This average is used as context, not as a trigger.

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### 2) Burst Detection (Main Trigger)

Detect bursts inside a short rolling window (250–500 ms).

#### SELL Signal (example):
Enter SELL if ALL conditions are met:
- ≥ 20 SELL trades within 500 ms (configurable)
- SELL trades ≥ 2× BUY trades
- SELL volume ≥ 70% of total volume
- Trade speed significantly higher than average
- Price already moves –1 tick

BUY signal is the exact inverse.

---

### 3) Decision Rule

Trade only if:
- Burst direction matches the average market bias
- Spread is below threshold
- No news / abnormal volatility
- Cooldown respected

This avoids trading noise and fake pressure.

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## Trade Management

Each trade:
- Entry: market or aggressive limit
- Take Profit: +1 to +2 ticks
- Stop Loss: –1 to –2 ticks
- Timeout exit: close trade if no movement after 2–5 seconds

No holding, no averaging, no martingale.

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## Frequency Target

- 500–800 trades/day on average
- Higher during US session
- Small position size
- High-frequency, low exposure

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## Execution & Architecture

- Execution on MetaTrader 5 (IC Markets)
- Strategy engine can be:
- C++
- Python
- or hybrid
- Communication with MT5 via:
- DLL
- Socket
- ZeroMQ
- or equivalent low-latency bridge
- Must be VPS-friendly (New York / low latency)

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## Required Skills

- Algorithmic trading experience
- Time & Sales / Order Flow strategies
- Rithmic API (or similar institutional feed)
- Low-latency systems
- MT5 automation (MQL5 or bridge)
- Understanding of spread, slippage, execution risk

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## What You Must Explain in Your Proposal

1. Have you built Time & Sales–based bots before?
2. Have you worked with Rithmic or similar data feeds?
3. How would you detect bursts efficiently?
4. What language / architecture do you recommend and why?
5. Estimated price ($) and timeline for:
- MVP
- Stable production version

If this type of strategy is not your specialty, that is totally fine — just be honest.

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## Important Notes

- No martingale
- No grid
- No indicators
- No marketing bot

This is a serious technical HFT-inspired project, adapted for retail execution.

Looking forward to working with a professional developer.

Best regards,
Saïd

Project Scope and Objectives:

the target website URL: https://app.airdna.co

login requirements: create your own free account

exact data fields to extract :

at the market level (airDNA / find a market)
* market score
* annual revenue
- at the market level (airDNA / find a market / listings)
* total active listings
* total active listings; growth rate

at the market level (airDNA / find a market / submarket)
- market score

volume/frequency: one-time
output format: CSV
sample data: https://docs.google.com/spreadsheets/d/12vgIFmGFOgSUlm9e750ho3aV4yNXgoJub2Nv9f_LQOQ/edit?usp=sharing

Budget, Timeline, and Payment Structure
- Milestone 1: send test data before completing project
- Budget: please provide your best offer
- Timeline: please let me know when this could be completed by. people who can finish fastest will be prioritized

Technical lead / founding engineer for an applied AI fintech venture.
The team is targeting someone who combines strong product engineering with real data systems experience.

Profile:
- Senior full-stack engineer (5–7+ years) from a strong fintech or infrastructure company (Stripe / Plaid / Brex–type environment or similar caliber)
- Strong in React, TypeScript, and Python
- Comfortable building end-to-end systems
- Experience working with financial data, security, or compliance-heavy systems
- Experience designing data pipelines and handling non-trivial data volumes
- Comfortable building scoring / analysis systems (deterministic + model-assisted logic)
- Strong understanding of AI/LLM capabilities, trade-offs, and evaluation frameworks (not just API usage)
- Thinks in terms of system reliability, observability, and cost control
- Pragmatic builder — ships fast but with discipline
- High ownership mentality; proactive and product-aware
- Ideally interested in fintech / wealth management / B2B SaaS
- Master’s/PhD in CS (or similar) is a plus, but execution and commercial instincts matter more. Open to academic backgrounds if there’s applied, production experience.
- Ownership. Speed. Innovation. User Centered. Analytical and business oriented.

Looking for someone who wants meaningful ownership and long-term upside. Initially Part-time and milestone driven.

#collaboration #part-time

Rate depends on seniority & deliverables.

We’re hiring a Senior AI Demand Forecasting Lead to design and own a single, intelligent demand forecast across a complex omnichannel commerce ecosystem.

This is not a reporting role.
This is not a traditional planning role.

This person will build the forecasting brain that powers buying, production, replenishment, and inventory risk decisions across:

DTC eCommerce (Shopify)

9 owned retail stores

Multiple marketplaces flowing through Shopify

Amazon

Drop-ship marketplace(s)

Franchise locations operating on separate POS systems

The challenge: fragmented data, different fulfillment models, different inventory ownership structures — all needing to roll into one SKU-level forecast that reflects true demand, not just booked sales.

What You’ll Do
🔹 Build a Unified Forecasting System
Design an end-to-end demand forecasting architecture that consolidates all revenue channels into a single source of truth

Normalize and reconcile sales data across:

Owned inventory

Drop-ship demand

Franchise demand

Marketplaces with delayed or partial reporting

🔹 Apply AI & Advanced Forecasting Methods
Build SKU-level forecasts using:

Time-series models (Prophet, ARIMA/SARIMAX)

Machine learning models (XGBoost, LightGBM, LSTM where appropriate)

Incorporate:

Seasonality

Launch timing / time-on-shelf normalization

Promotion and discount lift

Channel-specific behavior

New SKU analog logic using attributes

🔹 Channel-Aware Demand Logic
Separate true demand from inventory-constrained sales

Avoid double-counting demand across channels

Translate external demand signals (Amazon, drop-ship, franchise) into internal production and buy recommendations

🔹 Data Engineering & Integration
Build automated pipelines pulling from:

Shopify APIs

Amazon Seller/Vendor data

POS systems (Lightspeed, external Shopify)

Existing Excel-based planning tools (as a bridge, not a crutch)

Reduce manual Excel dependency while respecting current workflows

🔹 Business Partnership
Work closely with Product, Merchandising

Explain forecasts in business language, not just models

Build trust and adoption across teams

What We’re Looking For
Must-Have

7–10+ years in demand forecasting, data science, or advanced analytics

Hands-on experience building forecasting models (not just consuming tools)

Strong Python skills (pandas, numpy, sklearn, statsmodels)

Experience working with messy, real-world commerce data

Omnichannel experience (DTC + retail + marketplaces)

Strongly Preferred

Shopify and Amazon data experience

Retail inventory mechanics (on-hand, on-order, lead times, base stock)

Experience modernizing or replacing Excel-based forecasting systems

Consumer goods or high-SKU environment experience

What This Role Is Not
❌ Not a reporting analyst

❌ Not a BI/dashboard-only role

❌ Not a traditional retail planner

❌ Not a “load data into a tool” job

This role builds the system.

Why This Role Is Different
High ownership and visibility

Real-world forecasting problems (not academic exercises)

Complex, interesting data

Direct impact on inventory risk, margin, and growth

Opportunity to architect something foundational

***Read this entire document before applying. Your proposal must reference specific details from this posting. Generic proposals will be rejected immediately.***


The Situation
We are a profitable precision nutrition meal delivery service. Entering an aggressive growth phase in 2026.

Our entire technology infrastructure has been managed by a single engineering contractor for three years:

One person controls all hosting, database access, credentials, and deployment pipelines
There is no documentation. No architecture diagrams. No runbooks
We do not know our hosting provider, configuration, or redundancy setup
We do not have a complete inventory of services, credentials, or integrations
No monitoring. No alerting. No disaster recovery plan
10+ years of customer, order, and financial data in a PostgreSQL database that no one on leadership can access directly
The contractor consistently exceeds budget caps without flagging overruns and slow-rolls access requests

This is an existential risk. You are the person who maps, secures, and hardens the infrastructure so it is no longer a black box controlled by one person.

You will work alongside a Senior Full-Stack Engineer (being hired in parallel). They own the application layer. You own the infrastructure layer. Together you eliminate the single point of failure and give leadership full visibility into the systems that run the business.





The Stack
Frontend: Custom-built application
Backend: Custom application layer, Rails and/or Node.js
Database: PostgreSQL, 10+ years of production data
Hosting: Cloud-hosted. Provider and configuration unknown
Migration: Moving to Shopify. Infrastructure must support the migration and post-migration architecture
Integrations: FedEx shipping, payment processing, SMS/email marketing, various operational tools. Full inventory unknown





Your First 30 Days: Concrete Deliverables

Deadline
Deliverable
Day 5
Complete infrastructure map: every server, database instance, DNS record, SSL certificate, CDN, load balancer, cron job, and third-party service. Where it runs, who pays for it, and who has the credentials.
Day 10
Credential and access audit: every account, API key, SSH key, and password. Where they are stored, who has them, which are shared, which have MFA, and which are single-person dependencies. Severity-rated risk matrix.
Day 15
Backup assessment: what is currently backed up, how often, where backups are stored, and whether anyone has ever tested a restore. If no verified backups exist, flag it as critical and propose a 48-hour fix plan.
Day 20
Monitoring and alerting MVP: basic uptime monitoring, database health checks, error rate tracking, and disk/CPU/memory alerts.
Day 30
Disaster recovery playbook: documented, tested with a restore drill, written so a non-DevOps engineer could execute it in an emergency. Includes RTO/RPO targets and escalation procedures.






After 30 Days
Credential management. Vault, AWS Secrets Manager, or equivalent. No more credentials in plaintext or shared accounts.
CI/CD pipeline. Staging environments, automated deployment, and rollback capability.
Infrastructure as code. Reproducible, version-controlled infrastructure. Nothing lives only in someone's head.
Migration support. Ensure hosting, database, and services are ready for the Shopify migration and post-migration architecture.
Cost optimization. Review and right-size all infrastructure spending. Present findings with recommendations.
Scale planning. Capacity modeling for 2x and 5x current volume. What breaks first and what to invest in before it does.





Requirements
Non-Negotiable
4+ years in DevOps, SRE, or infrastructure engineering. You have owned the reliability of production systems.
Deep experience with AWS, GCP, or Azure: provisioning, networking, IAM, security, cost management.
PostgreSQL administration: backup, replication, performance tuning, access control.
You have audited or taken over infrastructure from someone else with minimal documentation.
You write runbooks, architecture diagrams, and incident response procedures as standard practice.
You can explain infrastructure risks to non-technical stakeholders in plain language.
Upwork auto-tracked time. No exceptions.

Strong Preference
Docker, Kubernetes, or container orchestration experience.
Terraform, CloudFormation, Pulumi, or other IaC tools.
Experience supporting e-commerce platform migrations (Shopify or similar).
Data pipeline or ETL experience (BigQuery, Metabase, Fivetran).
Incident response and on-call experience.
DTC, subscription, or food tech background.





Automatic Disqualifiers
Do not apply if any of these are true:

You have never audited or inherited someone else's infrastructure
You do not document your work as you go
You are uncomfortable sharing access or credentials with team members
You cannot explain infrastructure concepts without jargon
You are not willing to use Upwork auto-tracked time





About the Company
$26M revenue, profitable, zero outside debt
16,000+ meals produced weekly across commercial kitchens
Nationwide DTC delivery in reusable glass jars
Precision nutrition with measurable health outcomes
2026: new channels, product lines, significant operational expansion
Lean team, high standards, direct communication, zero bureaucracy





How to Apply
We know most applicants will use AI to write proposals. That is fine. What is not fine is proposals with zero evidence of actual experience. Your screening question answers can only be good if they come from someone who has actually done this work.

Answer every screening question. Reference specific details from this document. Proposals that could apply to any DevOps role at any company will not be read.

What Happens After You Apply
30-minute video call. You walk through your screening answers live. If the details get vaguer than what you wrote, the interview ends.
Paid technical exercise (2-4 hours, at your rate). Access to a test environment. Produce a mini infrastructure audit: map what you find, assess risk, document findings for a non-technical audience.
Offer or pass. If the exercise confirms your application, we move to contract. If not, we pay you for the exercise and part ways.

No whiteboard questions. No trivia. We pay you to do a small version of the job and evaluate the output.

I’m seeking an experienced Python developer to help complete and harden a custom automation tool that converts Gravity Forms questionnaire exports into a proprietary structured file format for use in third-party software.

A working foundation already exists. This is not a greenfield project... the core generator, mapping documents, regression tests, and sample files are complete. I need someone to finish the remaining logic, improve accuracy, and make the system production-ready.

Project Goal:
Create a reliable tool that:
Takes Gravity Forms CSV exports
Applies mapping and transformation rules
Generates structured import files
Imports cleanly into external software with minimal manual correction

Current Status:
You will be provided with:
Existing Python generator (multiple versions)
Sample Gravity Forms exports
Reference (“gold standard”) output files
Detailed mapping spreadsheets
Written technical notes and requirements
Regression reports
The system already works partially. Your job is to close the gap.

• Describe your recent experience with similar projects

We're building a web application that helps trade associations and nonprofits clean up their Salesforce databases. We have the core data cleanup logic already built in Python and a web interface under development. We need an experienced Salesforce API developer to build the integration layer that connects our application to Salesforce.

I’m running a short, exploratory technical experiment to test an idea before committing to a larger build.The concrete case I want to explore is whether multiple existing image processing and computer vision tools—specifically YOLOv8-nano (ML-based object detection), OpenCV (rule-based image processing), and Segment Anything (foundation-model segmentation)—can be connected in a modular, inspectable pipeline, rather than being used independently as opaque black boxes. This experiment is intentionally small in scope and “quick-and-dirty.” That said, if feasibility is confirmed, the underlying concept has clear potential to evolve into a larger and more scalable, tool-agnostic system for composing and recombining decision logic across domains.

Third stage of project is custom built DataCenter Management Dashboard. It will interface with part I (Studio) and part II (UI) from previous work.

Totem-Dashboard will be used to configure and connect with Agents operating in the Cloud, or on commercially available mobile Robots. Dashboard will allow configuration of Totem Agents and Modules to be installed in any Data-Center, and later to work collectively with AI clusters.

The first Core module to integrate into the Totem-Dashboard will be a video-analytics product.

Chat Backend Developer for AI Agent Platform (80-100 hours)

Project Summary

I need an intermediate-level Python developer to build a WebSocket-based chat backend for my AI consulting platform. This is a well-defined RAG chatbot project using standard technologies (FastAPI, PostgreSQL, LLM APIs). You'll build the backend while I handle the WordPress frontend - clean separation of responsibilities, clear deliverables, 4-5 week timeline.

**Budget:** $3,000-3,500
**Timeline:** 4-5 weeks
**Experience Level:** Intermediate
**Project Size:** Medium

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What You'll Build

A production-ready chat backend that:
- Handles real-time WebSocket connections for chat
- Integrates with WordPress REST API for authentication and configuration
- Processes documents (docx, pdf) into vector embeddings
- Implements RAG: retrieves relevant document chunks + conversation history
- Calls LLM providers (OpenAI, Mistral, Gemini) via LiteLLM
- Tracks token usage and logs to WordPress
- Stores conversations with 4-month retention

Required Skills & Experience

**Must Have:**
- Python 3.x backend development
- PostgreSQL database
- **LLM/AI API integration (OpenAI, Mistral, Gemini, or similar)**
- **Vector embeddings and similarity search (pgvector, Pinecone, or similar)**
- WebSocket/real-time communication
- REST API integration

**Nice to Have:**
- FastAPI framework (Flask or Django also acceptable)
- RAG (Retrieval-Augmented Generation) system experience
- LiteLLM or multi-provider LLM libraries
- Document processing (PDF, Word extraction)

Key Deliverables

- WebSocket chat server with real-time messaging
- Document processing pipeline (docx, pdf → vector embeddings)
- RAG retrieval with PostgreSQL/pgvector
- LLM integration (OpenAI, Mistral, Gemini via LiteLLM)
- WordPress REST API integration
- Conversation persistence and token tracking
- Deployed and operational on VPS
- Basic documentation

**Estimated effort:** 80-100 hours over 3-4 weeks

Infrastructure

Deploy to a single VPS (DigitalOcean, Linode, or similar):
- 4GB RAM, 2 vCPU server
- PostgreSQL with pgvector
- Monthly hosting target: $25-50

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How to Apply

Please include in your proposal:
1. Links to chat/LLM projects you've built (and if under NDA please do not disclose any confidential information). Please describe the infrastructure used, why it was chosen, any findings you learned and why they were important.
2. Your questions about the requirements.


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Next Steps

I'll review proposals and schedule calls with top candidates in the coming weeks. Thank you in advance for your interest!

We are seeking a skilled GoHighLevel Automation Builder & AI Expert to enhance a sales and estimating process for a home services company. The ideal candidate will have a strong background in creating workflows, integrating AI functionalities, and optimizing automation for better lead generation and customer engagement. You will work closely with our team to design and implement innovative automation solutions that drive results. If you have a passion for technology and a knack for streamlining operations, we want to hear from you!

Hi I need a tool that converts .html into .exe and a supporting branding tool.

When the .exe runs, it opens the .html like an minimal browser app, with no trace of any URLs or links when mouse hovers over a link.

The idea is to make the .html feel like it's a program, rather than a webpage.

The .html itself will be made to look like an interactive interface with menu buttons on the left column, that loads an iframed video player.

I've attached full instructions with downloadable demo link inside, please read for full details.

Project Overview
We are looking for a freelancer to design and develop a Smart Home Simulation system with no physical hardware.
The project focuses on software simulation only.

The system should simulate room devices and sensors, integrate with IoT concepts, allow remote control of devices, and display real time energy consumption.

Project Objectives
Simulate a smart room environment
Integrate sensors and devices using IoT architecture
Control devices remotely including on and off and basic modes
Monitor and visualize device power consumption
Demonstrate smart automation scenarios such as HVAC and lighting control

Room Devices (Simulated)
The simulated room should include the following devices

3 lights
1 air conditioner
1 water heater

Each device must
Support on and off control
Show current power consumption
Be controllable manually and through automation rules

Sensors (Simulated)
The following sensors must be implemented

Temperature sensor for HVAC logic
Motion sensor for occupancy detection
Humidity sensor to support HVAC behavior
Power consumption sensor for energy monitoring
Distance or height sensor for simulated presence or object detection
Light sensor to detect ambient light level

Sensor values should update dynamically and be visible on the dashboard.

IoT and System Requirements
IoT based architecture using MQTT or REST API or a simulated cloud
Central controller or IoT gateway
Real time communication between sensors devices and controller
Dashboard to view sensor data control devices and monitor consumption
Basic automation rules engine

Required Block Diagrams

General Setup Block Diagram
Should include
Sensors layer
IoT gateway or controller
Backend or cloud layer if used
User interface or dashboard
Devices layer

Purpose is to explain the overall system architecture and data flow.

HVAC Block Diagram Implementation
Should include
Temperature sensor
Humidity sensor
Motion sensor
HVAC control logic
Air conditioner
Energy consumption monitoring

Example logic
If temperature is above a defined threshold and motion is detected then the air conditioner turns on
If no motion is detected the air conditioner turns off
Energy consumption is displayed in real time

Lighting Block Diagram Implementation
Should include
Light sensor
Motion sensor
Lighting control logic
Three simulated lights
Energy consumption monitoring

Example logic
If motion is detected and ambient light is low then lights turn on
If no motion is detected lights turn off

Deliverables
Complete smart home simulation including code and user interface
Block diagrams provided as image or diagram files
Clean and well structured source code
Documentation explaining setup and usage
Demo video or screenshots showing functionality

Description

I’m looking for someone with Canvas LMS experience to help build a biweekly student activity and progress report.

The goal is to understand what content students interact with and where students stop progressing in long-running (perpetual) courses.
Canvas’s built-in reports are too limited, so this must use Canvas Data 2 (CD2) combined with the Canvas REST API.

What You’ll Build
Biweekly CSV Report (Python)

Documentation:
https://developerdocs.instructure.com/services/canvas
https://developerdocs.instructure.com/services/dap/key-concepts

Link to Detailed Job Description: https://docs.google.com/document/d/1Ms9GSb6QcMyDbzngyyIeaHdOLT5ICQA-vPjoP5BPg_E/edit?usp=sharing

• When can you start?
• Any questions for me?
• Do you understand the job?

We are seeking a skilled backend developer to create the backend infrastructure for our innovative AI receptionist platform. This role involves designing and implementing APIs, managing databases, and ensuring seamless integration with front-end components. The ideal candidate will have a strong understanding of AI technologies and experience with scalable backend systems. If you are passionate about building cutting-edge solutions and have a proactive approach to problem-solving, we would love to hear from you!

About the project
We’re an automation consultancy. We’re looking for a senior developer to execute a turnkey project for one of our clients: a professional tax and accounting firm in Spain.
The client has 1.3 million files (555 GB) on a local Windows server: invoices, contracts, deeds, payroll, tax filings, balance sheets, annual accounts, and corporate documentation accumulated over 10+ years.
The project has two phases.
Phase 1 — Document organization and cleanup (4-6 weeks)
Goal: turn 555 GB of unstructured files into a clean, classified document base.
Deliverables:
∙ Deduplication script (exact hash + near-duplicates). Generates a report of candidates for deletion; nothing is deleted without validation.
∙ Automatic classification script by client, document type, year, and category. Combination of regex/heuristics (file names, paths, metadata) + batch LLM for ambiguous cases.
∙ Reorganization script: move and rename files according to a normalized structure. Full log of all movements.
∙ Separation of historical vs. active archive.
∙ Diagnostic report: inventory, statistics, resulting structure.
Requirements:
∙ Text extraction: pdfplumber/PyMuPDF (native PDFs), Tesseract or Azure Document Intelligence (scanned), python-docx/openpyxl (Office files).
∙ Must work against a Windows server via remote access.
∙ Robust execution: error handling, retries, logging. 1.3M files doesn’t tolerate fragile scripts.
Phase 2 — RAG-based intelligent query system (4-6 weeks)
Goal: enable the client’s team to locate any document or data point in seconds using natural language queries.
Deliverables:
∙ Ingestion pipeline: text extraction, chunking, embeddings, storage in vector DB.
∙ Query system: natural language question → answer with source document, exact page, and direct link. Response time 30 seconds.
∙ Automatic classification of new documents added to the server.
∙ User-level access control.
∙ Simple web interface (browser-based, no installation, user login).
∙ Technical documentation sufficient for maintenance.
Suggested stack (flexible):
∙ Embeddings: voyage-3, text-embedding-3-small, or similar.
∙ Vector DB: Qdrant or ChromaDB (self-hosted).
∙ Query LLM: Claude Sonnet API.
∙ Interface: Next.js, Streamlit, or equivalent.
∙ Deployment: on-premise or private cloud (TBD).
How we work
∙ We provide: VPN access to the server, point of contact at the client, organization criteria, API keys.
∙ You do NOT talk to the client directly. All communication goes through us.
∙ Weekly check-in meetings.
∙ We start with a pilot on a small folder (1 client, 1 year) before scaling to the full volume.
What we expect
∙ Autonomous execution with proactive communication if there are blockers.
∙ Clean, documented, deliverable code.
∙ Demonstrable experience with at least one RAG project in production (not demos or tutorials).
∙ Native or advanced Spanish (documentation is in Spanish/Catalan).

I am creating a database of several brands of canned foods. I need the user to search by image. That is:
1- the user uploads an image
2- AI identify which product in our database (based on the images that we have) corresponds to this product

note that sometimes 2 can look very similar but have a tiny difference in the text on the label. so the ai needs to be that accurate