Content Overview
Blockchain networks generate vast amounts of high-frequency data—daily, minute-level metrics, and diverse asset types surpassing traditional markets. By leveraging Python APIs to access this data, investors can perform robust quantamental analysis (quantitative + fundamental) to drive profitable strategies in cryptocurrencies and NFTs.
About the Author
Qian Chen, a quantitative engineer at a私募基金 (private equity fund) and bestselling author of Python for Trading, shares实战 (practical) techniques from his personal portfolio—achieving 50% annual returns through crypto/NFT investments.
Key Insights
1. Cryptocurrency Strategies with Python
- Utilize on-chain data (transaction volumes, wallet activity) to develop multi-strategy approaches for Bitcoin, Ethereum, and altcoins.
Book includes ready-to-use Python code for:
- Fetching historical price/volume data via third-party APIs (e.g., CoinGecko, Binance).
- Visualizing strategy performance with matplotlib/seaborn.
- Backtesting frameworks to evaluate risk-adjusted returns.
2. NFT Profit Blueprints
- Early-stage projects: Apply VC-style fundamental analysis to identify 10x opportunities (e.g., 1-month 10,000% returns案例).
- Established collections: Mine historical sales data with Python to optimize entry/exit parameters.
"Learn a method, not just a strategy—empower yourself to adapt in volatile digital markets."
Industry Praise
- "A systematic breakdown of crypto/NFT quant strategies... rare clarity." — 浑水 (finance columnist)
- "His risk-centric framework revolutionizes crypto project analysis." — 对冲基金经理 蔡嘉民
- "Bridges coding literacy with investment acumen." — 香港程式交易研究中心 歐陽一心
Book Highlights
✅ Python-Powered Analytics: Code snippets for automated data pipelines, strategy backtesting, and链上数据 (on-chain) modeling.
✅ Multi-Asset Coverage: From DeFi yield optimization to NFT floor-price arbitrage.
✅ Real-World Cases: Sandbox活跃钱包 (active wallets) vs. SAND price correlation; SOL trading signals via MTVL.
👉 Explore advanced crypto APIs for live data integration.
FAQ
Q1: How does quantamental analysis differ from pure algorithmic trading?
A: It blends фундаментальные (fundamental) metrics (e.g., project团队, tokenomics) with quantitative signals (e.g., MVRV ratios), offering a hybrid edge.
Q2: Can beginners implement these Python strategies?
A: Yes! The book includes step-by-step Jupyter notebooks with注释 (comments) for each coding block.
Q3: What’s the optimal hardware for running backtests?
A: A mid-tier cloud instance (e.g., AWS t3.xlarge) handles most链上数据分析 (on-chain analytics); local machines suffice for basic price modeling.
👉 Start trading with institutional-grade tools today.
目录 (Table of Contents)
VC-Style Crypto Selection Framework
- Intrinsic value assessment
- Metrics like NVT ratios, exchange net flows
On-Chain Data Pipelines
- API data harvesting (Glassnode, Dune Analytics)
- Identifying "爆升币" (explosive-growth coins)
- NFT Investment Tactics
-铸造 (minting) vs. secondary-market arbitrage
-香港电影 (Hong Kong cinema) NFT revival case study DeFi’s Societal Impact
- Post-war blockchain融资 (financing) trends
- Youth income diversification