How to Build a Stock Screener in Python | TA-Lib, yFinance & Technical Indicators

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Finding trades starts with a robust stock screener. In this tutorial, Mohak Pachisia, Senior Quant at QuantInsti, demonstrates how to code a full-featured market scanner in Python. You will learn how to pull multi-ticker data with yfinance, compute technical indicators with TA-Lib, and generate composite scores that highlight the most bullish and bearish names in the Dow Jones.

Access the Python Notebook here: https://blog.quantinsti.com/momentum-...

The session begins by explaining why a custom screener is superior to off-the-shelf platforms. Mohak then imports key libraries including Pandas, NumPy, Matplotlib, Seaborn, and TA-Lib, and shows how to download historical prices for dozens of symbols in one line of code. Next, you will calculate 50-day and 200-day moving averages, RSI values, and custom volume spike metrics. Each indicator is normalized to a minus-one to plus-one scale so they can be combined into a single ranking score.

With the scoring model in place, Mohak builds a heat map that instantly reveals long and short candidates. You will see how to interpret these rankings, how to adjust thresholds, and how to feed the screener into backtesting or live-trading workflows. The tutorial closes with guidance on scaling the framework to hundreds of stocks, ETFs, or crypto pairs, and how to automate alerts.

This video is perfect for algorithmic traders, portfolio managers, data scientists, and learners in the EPAT and Quantra programs who want to strengthen their research pipeline before backtesting a trading strategy.

What You Will Learn
-Why custom stock screeners improve idea generation
How to build a stock screener in python
How to collect multi-ticker price data efficiently with yfinance
-Calculating technical indicators such as moving averages, RSI, and volume spikes
-Normalizing indicators and creating a composite momentum score
-Ranking assets and visualizing results with a heat map
-Tips for scaling the screener to larger universes and automated alerts


About the Speaker
Mohak Pachisia is a Senior Quantitative Researcher at QuantInsti, specializing in trading strategy development, financial modeling, and quantitative research. Before joining QuantInsti, he worked in the Risk and Quant Solutions division at Evalueserve, where he also led the learning and development function for the Quant team.

Chapter Timestamps
00:00 Introduction to custom stock screeners
01:00 Why build your own market scanner
02:45 Setting up Python and key libraries
05:15 Downloading Dow Jones constituents with yfinance
07:45 Computing 50-day and 200-day moving averages
10:30 Measuring trend strength with distance and slope metrics
13:45 Adding RSI momentum and volume-spike indicators
17:30 Combining indicators into a composite ranking score
20:30 Visualising bullish and bearish stocks with a heat map
23:30 Interpreting screener output for trade ideas
26:00 Scaling the screener to more symbols and live data feeds
28:30 Summary and next steps

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