0xquqi

0xquqi / crypto-kol-quant

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首个将99位加密KOL交易经验LLM蒸馏为可回测量化因子的开源项目 | First to distill 99 crypto KOL trading experience into backtestable quant factors via LLM

46
12
89% credibility
Found Apr 12, 2026 at 46 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
HTML
AI Summary

This tool processes crypto price data through rules mimicking top traders' strategies to generate consensus signals, backtests, and visualizations focused on Bitcoin.

How It Works

1
🔍 Discover Trader Wisdom Tool

You stumble upon a clever system that boils down top crypto traders' ideas into easy-to-understand buy or sell signals for Bitcoin.

2
📊 Gather Market Prices

You collect simple daily price charts for Bitcoin and a few other cryptos, like grabbing snapshots of how prices moved over time.

3
⚡ Launch the Analysis

You hit go, and it instantly processes everything to test trader strategies and reveal what the crowd thinks right now.

4
👥 Check Group Consensus

See a snapshot showing how many traders say buy, sell, or hold, weighted by their past success, plus firing signals today.

5
📈 View the Live Chart

Open an interactive picture of Bitcoin's price with colored zones for expected moves and lines from top performers.

✅ Gain Daily Trading Edge

Now you know the collective smarts of dozens of experts, helping you spot opportunities without endless scrolling.

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Star Growth

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AI-Generated Review

What is crypto-kol-quant?

This project distills the trading experience of 99 crypto KOLs into backtestable quant factors via LLM—the first open-source effort to quantify subjective strategies. Feed it daily OHLC data for BTC and alts, and it spits out feature panels, factor scores, trader composites, and interactive HTML dashboards showing consensus signals, firing factors, and projected price boxes. Developers get a full pipeline from raw prices to visual trading oracles in Python with Plotly HTML output.

Why is it gaining traction?

It stands out by turning opaque KOL wisdom into measurable, IC-tested factors you can backtest across horizons, unlike generic TA libraries. The killer hook: one-command consensus snapshots blending 90 simulated trader signals (IC-weighted, school-bucketed) into gorgeous BTC charts with bull/bear boxes—perfect for crypto quants spotting edges fast. Early buzz comes from its novel LLM-to-quant bridge, even with just 46 stars.

Who should use this?

Crypto quant devs backtesting KOL-inspired factors on BTC/ETH/SOL. Trading bots builders needing pre-built regime, pattern, and macro signals. Researchers prototyping LLM-distilled strategies without starting from scratch.

Verdict

Grab it if you're experimenting with quant crypto edges—solid foundation despite 46 stars and thin docs; credibility score sits at 0.9%, signaling early-stage polish needed. Run the pipeline today for instant HTML insights, but expect to wire your own data feeds.

(187 words)

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