YuxinSUN89

YuxinSUN89 / quant-ohlcv-feature

Public

a collection of 300+ features/factors drawn from academic publications and leading industry reports

49
14
100% credibility
Found Apr 12, 2026 at 49 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A library of over 335 ready-to-use technical indicators that add insightful signal columns to financial price and volume data tables for analysis.

How It Works

1
🔍 Discover Market Insights Tools

You hear about a handy collection of simple tools that spot patterns in stock or crypto price charts, like momentum or trends.

2
📊 Gather Your Price History

You collect a simple table of past prices, highs, lows, closes, and trading volumes for your favorite assets.

3
⭐ Pick Your Favorite Signals

Browse easy categories like momentum boosters or trend spotters, and choose a few that match what you're curious about.

4
⚡ Add Magic Columns Instantly

With one quick command per tool, new insight columns appear in your data table, revealing hidden patterns.

5
📈 Spot Trends and Opportunities

Review the new columns to see buy signals, overbought warnings, or volume surges light up clearly.

🎉 Unlock Smarter Decisions

Your charts now tell a richer story, helping you make confident trades or predictions with fresh eyes.

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AI-Generated Review

What is quant-ohlcv-feature?

This Python library delivers 335+ technical indicators for OHLCV data, pulling from academic papers and industry reports into a single 300+ collection worldwide. Drop a function call on your pandas DataFrame with open, high, low, close, volume, and optional quote/taker volumes, and it appends a new column with the computed signal—no setup hassles. It streamlines feature engineering for trading ML pipelines on crypto or stocks.

Why is it gaining traction?

Uniform API means every indicator uses the same `signal(df, n, factor_name)` call, so you iterate categories like momentum or volatility without learning curves. Self-contained functions clean up temps and handle edge cases like zero-division, saving hours versus coding from scratch or wrangling TA-Lib. Breadth stands out—121 momentum variants alone beat most libs.

Who should use this?

Quant traders backtesting strategies on historical bars, ML engineers featurizing tick data for price prediction models, or crypto analysts automating signals in pipelines. Ideal if you're scanning papers for obscure factors like DBCD divergence or Amihud liquidity but hate reimplementing.

Verdict

Grab it for quick breadth in prototypes—low 49 stars and 1.0% credibility score flag early maturity with thin tests/docs, but the collection shines for discovery. Scale to production after your own validation.

(198 words)

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