Crypto Quantitative Analysis
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About this skill
The problem
Crypto analysis is often harder than calculating indicators. Common friction comes from unstable data sources, network restrictions, different token categories, and on-chain fundamentals that are hard to compare with price data. A single exchange quote can be delayed, isolated, or flaky; CoinGecko alone may miss exchange pair details; and using the same generic indicators for DeFi, Meme, L1, and L2 tokens can produce misleading conclusions.
How it works
- Multi-source market data: cross-validates
CoinGeckoaggregate prices with direct exchange data fromBinance/BybitorGate/OKX; deviations above2%are flagged, and deviations above5%trigger strong warnings. - Network awareness: detects global versus China network environments, prefers
Gate/OKXwhere available, keepsCoinGeckoas a cross-check, and uses progressive3s→5s→10stimeouts. - Quant analytics: supports
RSI,MACD, Bollinger Bands, correlation, volatility, return distribution, trend regression, and backtests for moving-average, RSI, and Bollinger strategies. - Portfolio risk: provides Markowitz optimization, risk parity, and
VaRfor comparing risk-return trade-offs across multiple assets. - Category fundamentals: switches frameworks by token type, such as TVL/MC and protocol fees for DeFi, community and holder concentration for Meme, and ecosystem TVL plus active addresses for L1/L2.
- On-chain data: pulls protocol TVL, fees, revenue, and protocol/chain rankings from
DeFiLlama, with explicit status when unavailable.
Outputs are typically JSON, then translated into Chinese interpretations so engineers can inspect fields and reasoning.
Boundaries and caveats
It does not cover stocks, securities, forex, commodities, or company financials, and it does not predict prices. Price and on-chain data can be delayed; CoinGecko free API has rate limits; some small-cap tokens may lack active pairs or on-chain metrics. Results are based on historical data and are better used for research, backtesting, and cross-validation, not as direct trading instructions.
Use Cases
- Cross-check BTC and ETH price feeds to spot deviations and identify stale data sources.
- Pull AAVE's DeFi TVL, protocol fees, and TVL/MC ratio to assess whether it is undervalued.
- Backtest an BTC RSI mean-reversion strategy and review Sharpe ratio, drawdown, and returns.
- Compute the BTC, ETH, SOL correlation matrix and derive a risk-parity allocation for diversification.
Best For
- Quant research engineers who need to combine exchange prices, technical indicators, and backtest outputs in one workflow.
- On-chain data analysts who need DeFi TVL, protocol fees, revenue, and chain rankings.
- Crypto portfolio strategists who need correlation, risk parity, and VaR for multi-asset allocation.
- Technical trading researchers who use RSI, MACD, and Bollinger signals to assess short-term trend context.
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