You will get ML-Enhanced Algorithmic Trading Platform - TradePulse
Project details
Complete algorithmic trading platform with machine learning integration. Three tiers available:
STARTER TIER: Core Rust trading engine with momentum strategies, SQLite database, paper trading support, and basic documentation.
STANDARD TIER: Full system with 13 microservices, ML pipeline (FinBERT sentiment analysis, LSTM price prediction, PPO reinforcement learning), PostgreSQL/TimescaleDB databases, Charles Schwab API integration, and comprehensive documentation.
ADVANCED TIER: Everything in Standard plus custom strategy development, deployment assistance, training documentation, and extended support.
The system processes real-time market data, generates trading signals using 40+ technical indicators and ML models (for STANDARD TIER+), manages risk through position sizing and stop losses, and executes trades through broker APIs. Built with Rust backend, Python ML services, and Docker deployment.
Includes complete source code, database schemas, API documentation, and setup scripts. Compatible with Charles Schwab API or can be adapted for other brokers. Supports both paper trading and live trading environments.
STARTER TIER: Core Rust trading engine with momentum strategies, SQLite database, paper trading support, and basic documentation.
STANDARD TIER: Full system with 13 microservices, ML pipeline (FinBERT sentiment analysis, LSTM price prediction, PPO reinforcement learning), PostgreSQL/TimescaleDB databases, Charles Schwab API integration, and comprehensive documentation.
ADVANCED TIER: Everything in Standard plus custom strategy development, deployment assistance, training documentation, and extended support.
The system processes real-time market data, generates trading signals using 40+ technical indicators and ML models (for STANDARD TIER+), manages risk through position sizing and stop losses, and executes trades through broker APIs. Built with Rust backend, Python ML services, and Docker deployment.
Includes complete source code, database schemas, API documentation, and setup scripts. Compatible with Charles Schwab API or can be adapted for other brokers. Supports both paper trading and live trading environments.
AI Development Type
Deep Learning, Model Tuning, Recommendation SystemAI Tools
PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$15,000
|
Standard
$35,000
|
Advanced
$50,000
|
|---|---|---|---|
| Delivery Time | 14 days | 20 days | 70 days |
Number of Revisions | 2 | 3 | 5 |
AI Model Integration | - | ||
Detailed Code Comments | |||
Knowledge Graph | - | - | |
Model Documentation | |||
Ontology | - | - | |
Source Code | |||
Taxonomy | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$3,500 - $5,000
Additional Revision
+$2,500Frequently asked questions
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JA
Joey A.
Dec 10, 2025
Build a Web scraper for finding URL's to match existing DB items
Daniel did an amazing job doing exactly what I needed. And fast too! Would definitely recommend, and will be back.
About Daniel
Software Engineer | Quantum Computing & AI Integration
Gaithersburg, United States - 3:18 pm local time
I also have hands-on experience building and modernizing websites for small and medium-sized businesses, as well as developing mobile applications for iOS and Android.
Whether you need to develop a next-generation AI solution, explore quantum computing workflows, or bring your software vision to life, I can help.
* Experienced with Python, C#, C, Rust, Java, Swift, SQL, and mobile app development (iOS/Android)
* Skilled in software security, database architecture, and quantum-hybrid pipelines.
* Full-cycle development — from design to deployment and maintenance.
* Regular communication is important to me, so let's keep in touch.
Steps for completing your project
After purchasing the project, send requirements so Daniel can start the project.
Delivery time starts when Daniel receives requirements from you.
Daniel works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Analysis & Environment Setup
Review client's broker API credentials, risk parameters, and deployment environment. Set up Docker development environment, configure databases, establish secure API integrations. Create project timeline based on selected tier.
Core Infrastructure Deployment
Deploy PostgreSQL, TimescaleDB, Redis, and Kafka infrastructure. Set up API Gateway with authentication. Configure Symbol Master and Market Ingest services. Establish real-time data feeds and basic health monitoring.