Many traders start their systematic trading journey in Python. Python has emerged as a favorite tool for traders, thanks to its versatility and extensive library support.
It’s easy to learn. It’s simple to read. It’s flexible, powerful, and perfect for research. It is very easy express trading logic. But as strategies evolve, many traders hit the same wall with Python:
“I don’t want to maintain an entire trading infrastructure just to test and deploy strategies.”
If that sounds familiar, this guide is for you.
In this article, I’ll walk through how to create a custom Python trading signal and use it inside Build Alpha, so you can keep Python where it shines—signal research—and let Build Alpha handle strategy generation, robustness testing, and deployment.
Why Python Alone Becomes a Bottleneck
Python is excellent for:
- data exploration
- understanding
- indicator research
- prototyping new ideas
But a full Python trading stack often means:
- writing and maintaining your own backtester
- handling data alignment and survivorship bias
- building robustness tests (Monte Carlo, noise, permutations)
- creating execution adapters for each platform
- managing positions and ticker changes
- managing logs, errors, and edge cases
For many traders, this becomes engineering work instead of trading research. It takes all the enjoyment out of the creative process of signal discovery. That’s where a hybrid workflow makes sense.
The Hybrid Approach: Python for Signals, Build Alpha for Everything Else
Instead of choosing between Python or a no-code platform, you can combine both:
Use Python to define your custom signal.
Use Build Alpha to generate, validate, and export full strategies.
This gives you:
- full control over signal logic
- creative flexibility to express your ideas
- no need to build or maintain infrastructure
- systematic validation instead of one-off backtests
- powerful C++ simulation engine
The Best of Both Worlds: A Python Trading Strategy Builder
Use Python to create custom trading signals, and let Build Alpha handle strategy building, testing, and execution. Here’s how:
Step 1: Create a Custom Python Trading Signal
Your Python signal can be as simple or complex as you want. That’s really the point.
For example:
- a proprietary indicator
- a regime filter
- a statistical condition
- a feature derived from external data
All that matters is that the signal outputs a time-aligned series (true/false or numeric) that corresponds to market data. At this stage, Python is doing what it does best: research and signal creation.
How to Create a Custom Python Trading Signal in Build Alpha
In Build Alpha, go to File >> Custom Signals Editor and create a new Python signal. Give it a name and hit the Create button.
Then Build Alpha will open a Python template to add your custom logic. Build Alpha’s Python installation already has pandas, ta lib, scikit learn and other popular libraries included. However, traders can always add whatever libraries they need using pip install. Full details here: Using Python in Build Alpha
Benefits of Custom Python Signals in Build Alpha
From Build Alpha’s perspective, your Python signal becomes just another signal along Build Alpha’s built-in library of 7,000+ trading signals. Your Python signal:
- can be combined with other conditions
- used as an entry, exit, or filter
- tested across different markets and timeframes
- optimized
To allow Build Alpha to optimize your custom parameters, simply add your parameters in between these two comments in the Build Alpha generated template.
Then in your custom script make sure to use the parameter name you added. Here’s an example and screenshot showing proper use (yes, this could be vectorized and deal with NaNs).
df1[“SMA”] = talib.SMA(df1[“Close”].values, sma_length)
Signal = [1 if c > sma else 0 for c, sma in zip(df1[“Close”], df1[“SMA”])]
Then inside Build Alpha’s Strategy Builder go to Custom signals, select your Python signal (PythonPostExample from above), and set parameter ranges in the upper-right. Below will optimize this moving average signal from 5 to 25 stepping by 5. This gives Build Alpha the ability to use moving average lengths of 5, 10, 15, 20, and 25.
Step 2: Generate Strategies Using Your Python Signal
With your custom signal added, Build Alpha’s can now:
- generate thousands of strategy variations
- combine your signal with other conditions
- explore parameter ranges systematically
This is where Build Alpha replaces the part of Python that’s hardest to scale: combinatorial exploration.
Instead of manually looping through variations in (slower) python code, you define constraints and let Build Alpha’s C++ engine do the work.
Do I need to do any data manipulation? What historical data is available?
Build Alpha has historical data from many sources not just import yfinance. However, you can do that, too. You can also connect Build Alpha to brokers and data providers through their apis. Here’s a current list of what Build Alpha supports.
The data is provided by reliable brokerage and data sources and checked on the Build Alpha side as well. This reduces data wrangling overhead. Additionally, this helps mitigate common pitfalls (alignment, survivorship, look-ahead), depending on data/settings. No more print statements, downloading csv files or df.head() to inspect.
Step 3: Validate Robustness (Without Writing More Code)
This is where many Python-only workflows break down. Robustness testing is about evaluating whether a strategy is likely to survive across alternate market conditions—not just the historical path it was trained on. Generating multiple alternate “realities” (via techniques like noise tests, Monte Carlo resampling, or parameter perturbations) and re-trading a strategy across them is computationally expensive and time-consuming to build and maintain in pure Python.
Build Alpha includes built-in robustness testing, such as:
- Out-of-sample validation
- Monte Carlo trade reordering
- Noise Test
- Walk Forward Optimization
- Vs Random Benchmarking
These tests help answer the most important question:
Is this strategy likely to survive beyond the backtest?
All without writing additional Python infrastructure. Read here for more on Robustness Testing.
Automating Robustness Tests
It is an arduous process to simulate a strategy, run a series of stress tests, evaluate performance and verify results. Build Alpha enables traders to set automated filters for performance metrics, growth measures, and robustness tests so the only systems that are returned by the strategy engine are ones that meet your profitable and risk criteria. Things have never been faster than they are these days.
Step 4: Export Trading Strategies to your Trading Platform
Once a strategy passes your validation rules, Build Alpha can export it to supported platforms with the click of a button. Code generators exist in the lower right of the results window for the currently supported trading platforms:
- MetaTrader (Expert Advisors)
- ProRealTime (ProOrder / ProBuilder)
- MultiCharts (PowerLanguage)
- TradeStation (EasyLanguage)
- NinjaTrader (NinjaScript)
- Interactive Brokers (Python)
- TradingView (Pine Script)
After generating Python code, the trader can enable it to run on Interactive Brokers simulated account or live trading account. Once a strategy or portfolio of strategies is running the trader can view their orders inside Interactive Brokers’ Trader WorkStation (TWS).
Why This Workflow Appeals to Python-Savvy Traders
This hybrid approach is ideal if:
- you’re comfortable writing Python indicators or signals
- you don’t want to maintain a full backtesting engine
- you don’t want to write buy and sell order logic
- you want consistent robustness testing
- you want to deploy to multiple platforms without rewriting code
- you prefer a repeatable process over ad-hoc scripts
In short: Python for creativity, Build Alpha for process.
Final Thoughts
You don’t need to abandon Python to scale your trading research. In fact, I use it every week. That’s why I added the ability to extend Build Alpha with Python signals.
By importing custom Python trading signals into Build Alpha, you get the best of both worlds:
- flexibility and control in research
- structure, validation, and scalability in production
If you’ve ever felt stuck between “pure Python” and “no-code”, this hybrid workflow could be the missing middle ground.
Need an introduction to Python? Same Signal in Excel, Python, Build Alpha
Frequently Asked Questions
No. Build Alpha is designed to complement Python, not replace it. You can develop your custom signal logic in Python and import the signal into Build Alpha, where it becomes a building block for strategy generation, validation, and export—without rewriting your entire strategy in code.
You can import a wide range of Python-based signals, including custom indicators, regime filters, statistical conditions, machine learning, or proprietary features. As long as the signal produces a time-aligned output (for example, boolean or numeric values), it can be used within Build Alpha. Knowledge is the only limiting factor.
No. Once your Python signal is imported, Build Alpha handles backtesting and robustness testing using its built-in tools through a no-code (point-and-click) interface.
This workflow is designed to work alongside Python. Python remains ideal for research and signal creation, while Build Alpha provides a structured environment for strategy generation, validation, and deployment at scale.
This approach is ideal for traders who are comfortable writing Python signals but don’t want to build, maintain, or scale a full trading infrastructure. It’s especially useful for those who want a repeatable, disciplined process rather than a collection of one-off scripts.
Yes, if you create as a position sizing scaler then you can hit Display and visualize them.
Yes. Many traders use Python to create rolling signals based on metrics like volatility, returns, or regime changes for individual instruments such as AAPL. Build Alpha will use whatever symbol(s) is selected in the main interface.
No. As long as the script runs start to end without error then BA can access to generate signals. However, one note to point out is following best practices may reduce run time which may be important. Other performance topics are way out of the scope of this post!
