Code Your Strategy. Deploy It Live. All with Python.
Phoenix Python Build gives you a powerful, cloud-based development environment to write, test, and automate your trading strategies using Python. No servers. No infrastructure. Just pure algo-building power.
- Phoenix Python Build
What is Phoenix Python Build?
Phoenix Python Build is the go-to solution for traders and developers who want to write their own trading logic in Python and automate its execution end-to-end—without worrying about servers, hosting, or integrations.
What you get?
Full control over your code
Write, test, and deploy your own Python-based trading strategies with no limitations or vendor lock-in.
Access to real market data
Seamlessly integrate real-time and historical market data directly into your algorithms.
Secure cloud-based execution
Run your trading logic in a secure, managed environment without needing to provision or maintain infrastructure.
Backtesting, paper trading & live trading
Validate your strategies against historical data, simulate real-time conditions, and go live with confidence—all from the same platform.
Complete library & ML support
Create your strategy logic from scratch or use starter templates. Fully customisable.
💡 Whether you're building simple moving average strategies or complex ML models— Phoenix handles the rest.
Who is it for?
If you think in code, this is your playground.
Quant Developers & Algo Traders building systematic strategies
PYTHON PROGRAMMERS breaking into trading
dATA SCIENTISTS exploring the market
RESEARCH ANALYSTS automating their logic
Why Phoenix python Build?
Write in Python, Your Way
Create your strategy logic from scratch or use starter templates. Fully customisable.
Full Library Support
Use NumPy, Pandas, TA-Lib, SciPy, scikit-learn, TensorFlow, and more.
Backtesting & Paper Trading
Test strategies against historical and live market data before going live.
Cloud-Based, Zero Infra Management
No devops. No server hosting. Code and deploy straight from your browser or CLI.
Jupyter Notebook Integration
Develop interactively, experiment faster, and visualise results as you go.
Command Line Interface (CLI) Support
Use terminal-based tools for speed, repeatability, and integration with your dev setup.
Machine Learning Ready
Train and deploy ML models directly within your strategy code.
Live Market Execution
Go live with one click. Strategies run on enterprise-grade, low-latency infrastructure.
How It Works
A simple 4-step flow
Step 1: Code Your Strategy
Use your own logic or modify from a template. Import any libraries you need.
Step 2: Test
Backtest on historical data or simulate in real-time with paper trading.
Step 3: Deploy Live
One-click deployment. AlgoBulls handles hosting, execution, and trade management.
Step 4: Monitor Performance
Access logs, analytics, trade summaries, and debug tools in real-time.
Use Cases
Indicator-Based Strategies
Write strategies using RSI, MACD, Bollinger Bands, or your custom logic.
Multi-Leg Option Strategies
Create condition-based option spreads, straddles, or combo setups.
ML-Powered Forecasting
Use time-series prediction models for directional trading.
Custom Signal Integration
Pull data from APIs or screener signals and convert them into executable logic.
Templates & Starter Packs
We offer a library of ready-to-modify templates to help you get started faster
SMA/EMA Crossover
A strategy based on the crossover of Simple Moving Average and Exponential Moving Average indicators.
RSI Reversal Strategy
A strategy that identifies potential market reversals using the Relative Strength Index indicator.
Bollinger Band Breakout
A strategy that trades breakouts from Bollinger Bands to capture significant price movements.
Mean Reversion with ML
A machine learning enhanced strategy that identifies and trades mean reversion opportunities.
Option Straddle with Condition Logic
An options strategy that implements straddle positions based on customisable market conditions.
TESTIMONIALS
Don’t just take our words for it
“The best part about Phoenix Python Build is I didn’t have to worry about infra or deployment. I focus purely on the logic.”
“Being able to use scikit-learn and pandas in a live-deployed strategy was a game-changer for me.”
“CLI + Jupyter + Templates = Smoothest dev environment I’ve used in trading.”
Frequently Asked Questions
Yes, most major data science and trading libraries are supported out-of-the-box. In case they are not available, we can help you integrate that.
No. It’s entirely cloud-based. You can write, test, and deploy from your browser or CLI.
Absolutely. You can test with real historical data and simulate in live market conditions before going live.
Yes. You can train and integrate ML models using libraries like scikit-learn, and TensorFlow.
You’ll get access to a full console with logs, trade summaries, execution reports, and analytics.
Build with Code, Not Limitations
Start Coding Your Strategy in Minutes
Phoenix Python Build gives you the tools, templates, and tech to code, test, and run your strategies—without managing a single server.