ysonglala

ysonglala / b1-pipeline

Public

b1战法

20
6
100% credibility
Found Apr 05, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A semi-automatic research tool that processes A-share daily data to select and score stocks matching B1 pullback criteria, outputting charts and shortlists for human review.

How It Works

1
📖 Find the Stock Picker Tool

You stumble upon this helpful GitHub project that scans Chinese stocks for promising pullback chances.

2
🛠️ Prepare Your Computer

You download the files and set up the simple tools it needs to run smoothly on your machine.

3
📡 Link to Market Data

You sign up for a free service that provides daily stock prices so the tool can stay up to date.

4
📥 Grab Fresh Stock Info

With one command, you pull in the latest prices, volumes, and details for all major stocks.

5
🔍 Spot Promising Patterns

The tool crunches numbers to find stocks with low momentum signals, good support levels, and shrinking volume—your first list of candidates appears!

6
📈 View Trend Charts

It creates clear pictures of price movements, lines, and indicators for the top candidates to review.

7
⭐ Get Your Top Picks List

You receive a scored ranking with strengths, risks, and summaries, highlighting the best 10-20 stocks for closer looks.

🎉 Daily Shortlist Ready

Now you have a consistent, quick way to find high-potential stocks, making your research faster and more reliable.

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Star Growth

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AI-Generated Review

What is b1-pipeline?

This Python pipeline screens A-share stocks for "B1" setups—low J-value KDJ bounces near trend lines with shrinking volume—via a semi-automated daily workflow. It fetches data from Tushare, computes indicators like EMAs and MAs, filters candidates, exports annotated charts, and scores shortlists into JSON for human review. You get consistent top lists and visuals to speed up manual picks, without auto-trading risks.

Why is it gaining traction?

Staged CLI commands let you run incremental fetches and light preprocesses for quick daily scans, scaling to full reviews only on promising codes. YAML configs tweak rules like J thresholds or board-specific amplitude limits, favoring recall to catch edge cases. Sample outputs and regression scripts help validate tweaks fast.

Who should use this?

A-share day traders chasing technical bounces who hate rebuilding screens daily. Quant hobbyists refining B1 rules on historical data. Researchers needing reproducible pipelines for backtesting indicator combos like white/yellow line proximity with volume profiles.

Verdict

Solid research skeleton with tests, docs, and MIT license, but low maturity (20 stars, 1.0% credibility score) means expect iteration. Fork it if A-shares are your game; skim examples before diving in.

(187 words)

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