Your Strategy. Your Code. Fully Automated.
Phoenix Python Build gives you a Python-native, ML-ready, CLI-compatible environment to build, backtest, and deploy your own strategies—without managing servers or infrastructure.
Built for Coders. Trusted by Quants.
Full Python Control
Write real Python code with complete access to numpy, pandas, and other data science libraries.
Backtesting & Paper Trading
Test your strategies against historical data and simulate real-time trading.
One-Click Deployment
Go from development to production with a single command.
No Infrastructure Headaches
We handle the infrastructure. You focus on your trading logic.
Common Challenges Developers Face
The Challenges
How AlgoBulls Solves This for You
The Solutions
Building execution infra from scratch is time-consuming
No support for ML/AI libraries in most algo tools
Backtesting systems are clunky or inaccurate
Managing servers & brokerage APIs is a nightmare
Scaling a strategy securely takes too long
Code in Python with Full Library Support
Use Jupyter or CLI to develop & execute
Backtest with real historical market data
Paper trade in live environments
Deploy with 1 click—no infra setup
Monitor logs, trades, performance in real-time
What You Get as a Developer
Python-Native Environment
Write strategies the way you want—clean, modular, and fully open-ended.
Jupyter Notebook Integration
Perfect for iterative strategy development, visualisation, and experimentation.
Command Line Interface (CLI)
Use CLI to push code, deploy versions, schedule backtests, and more.
ML & Quant Libraries Included
NumPy, Pandas, SciPy, TA-Lib, scikit-learn, TensorFlow, Keras and more.
Institutional-Grade Backtesting Engine
Accurate, event-driven backtesting to simulate real-world fills.
Zero DevOps Burden
No hosting. No brokerage integrations. No cron jobs. You write the code, we run the infra.
USE CASES
Build ML-based forecasting models and auto-deploy them
Run multi-indicator strategies with fine-tuned parameters
Use Jupyter to test cross-market strategies before live deployment
Ingest signal data via APIs or CSVs, convert into trades
Create options strategies with dynamic entry & exit triggers
Tools You’ll Love
ML-Ready Python Support
Build sophisticated trading strategies with full Python support and integrated machine learning capabilities.
Backtest / Paper Trade / Live Trade Modes
Test your strategies in multiple environments before deploying to live markets.
Real-Time Strategy Logs
Monitor your strategy performance with detailed, real-time logs and insights.
CLI for Fast DevOps-Free Workflows
Streamline your development process with our powerful command-line interface.
Live Dashboard with Analytics
Visualise performance metrics and trade activity with our comprehensive dashboard.
TESTIMONIALS
Don’t just take our words for it
“Phoenix Python Build is literally what I was trying to build on my own—only cleaner, faster, and with better support.”
“I’m running ML models on price patterns and deploying them daily without touching a single server.”
“The CLI + Jupyter combo is perfect. I can backtest and push to production in one flow.”
FREQUENTLY ASKED QUESTIONS
Yes. You get a Python-native environment with support for most major libraries.
Use our built-in backtesting engine or run paper trades in real-time conditions.
No problem. You can work entirely in Jupyter or use our CLI to deploy and manage.
Yes. You can use APIs to push signals, receive logs, monitor trades, or integrate external triggers.
Not at all. AlgoBulls handles infrastructure, execution, and brokerage connectivity.
The Power to Code. The Platform to Run.
Phoenix Python Build is built for developers, by developers.
No more building infra from scratch. No more limitations.