Agent Skills

crypto-regime-analyzer

researchtradermonty2.1K installs

Quantifies crypto market regime health using free, keyless public data (CoinGecko + Binance funding). Generates a 0-100 composite score across 6 components (100 = risk-on) with a posture recommendation. No API key required. Use when user asks about crypto market conditions, whether it's alt season, BTC dominance, crypto risk-on vs risk-off, funding rates, or whether crypto exposure should be increased or reduced.

Install

npx skills add https://github.com/tradermonty/claude-trading-skills --skill crypto-regime-analyzer
SKILL.md

Crypto Regime Analyzer Skill

Purpose

Quantify the crypto market regime using a data-driven 6-component scoring system (0-100). This is the crypto analog of market-breadth-analyzer + exposure-coach: it answers "what posture does the crypto market currently support?" before any coin-level analysis happens.

Score direction: 100 = Maximum risk-on health (broad participation, healthy trend, sane leverage), 0 = Critical risk-off.

No API key required — uses CoinGecko's free public API and Binance's public futures endpoint.

When to Use This Skill

  • User asks "Is crypto risk-on or risk-off right now?" or "How healthy is the crypto market?"
  • User asks "Is it alt season?" or about BTC dominance direction
  • User asks whether funding rates are overheated
  • User wants an exposure posture for a crypto sleeve before screening individual coins
  • User wants a daily crypto regime check alongside the equity market-regime-daily workflow

What This Skill Does NOT Do

  • No coin picks, no buy/sell signals, no price targets
  • No execution or portfolio changes — regime description only
  • Human decision gates remain central, consistent with the project vision

Prerequisites

  • Python 3.9+ with requests (live mode only; offline mode is stdlib-only)
  • Internet access to api.coingecko.com and fapi.binance.com (live mode)
  • No API keys required

Component Model

# Component Weight Question it answers
1 BTC Trend Structure 25% Is the reserve asset's primary trend intact? (price vs 50/200DMA stack, 200DMA slope)
2 Alt Breadth Participation 20% How broadly are alts participating? (% of top-N above 200DMA, 50DMA confirmation)
3 BTC Dominance Regime 15% Where is capital rotating? (dominance direction interpreted jointly with BTC trend)
4 Perpetual Funding Regime 15% How crowded is leverage? (avg funding across majors; contrarian at extremes)
5 Drawdown & Volatility Position 15% Where are we in the cycle? (drawdown from 1y high, realized vol percentile)
6 Momentum Thrust / Washout 10% Short-horizon confirmation (% of universe positive over 30d)

Missing components have their weight proportionally redistributed (same convention as market-breadth-analyzer). Full scoring logic: references/crypto_regime_methodology.md.

Regime Zones

Score Zone Posture
80-100 RISK_ON Broad risk-on conditions observed; review risk limits before decisions
40-79 NEUTRAL Mixed conditions observed; no strong regime conclusion
0-39 RISK_OFF Defensive market conditions observed; review existing risk controls

These are heuristic descriptive bands, not validated allocation rules. See references/VALIDATION.md for the current evidence boundary and the artifacts required before making quantitative performance claims.


Execution Workflow

Phase 1: Run the Analysis Script

Live mode (fetches CoinGecko + Binance; first run of the day takes ~2-4 minutes at the default --top-n 20 due to free-tier rate-limit throttling; same-day re-runs hit the cache and are instant):

mkdir -p reports/<routine-or-date>
python3 skills/crypto-regime-analyzer/scripts/crypto_regime_analyzer.py \
  --output-dir reports/<routine-or-date>

Offline mode (no network; snapshot schema in the methodology reference):

python3 skills/crypto-regime-analyzer/scripts/crypto_regime_analyzer.py \
  --input-json snapshot.json \
  --output-dir reports/<routine-or-date>

Options: --top-n <int> universe size (default 20), --cache-dir <path> fetch cache location (default .crypto_regime_cache), --quiet.

Phase 2: Interpret the Output

The script writes crypto_regime.json (machine-readable, for chaining into other skills) and crypto_regime.md (one-page report), and prints a one-line summary:

CRYPTO REGIME: NEUTRAL (score 68.4/100) — Mixed conditions observed; no strong regime conclusion

When presenting results, lead with the zone and posture, then explain the 1-2 components most responsible for the score using their signal strings. Flag any components reporting data_available: false and what that means for confidence.

Phase 3 (optional): Feed Downstream

The JSON composite can slot into an exposure-coach-style posture summary as one descriptive crypto-market input. It must not independently authorize, block, size, or execute a trade.

Output

The script writes two artifacts to --output-dir and prints a one-line summary for workflow chaining:

  • crypto_regime.json — full machine-readable analysis: metadata, per-component results (score, signal, data_available, component-specific fields), and the composite block (score, zone, guidance, effective_weights).
  • crypto_regime.md — one-page report: composite score with zone bar, posture line, per-component table (weight / score / signal), and confidence notes.
  • Console: CRYPTO REGIME: <ZONE> (score <N>/100) — <posture> plus warnings for any skipped components.

Resources

  • references/VALIDATION.md — validation status, evidence boundary, and reproduction requirements.
  • references/crypto_regime_methodology.md — full scoring rationale, every threshold table, the offline snapshot JSON schema, and the live data-source endpoint list.
  • scripts/crypto_regime_analyzer.py — CLI orchestrator (entry point).
  • scripts/data_client.py — CoinGecko/Binance fetchers, per-day cache, dominance history accumulator, offline loader.
  • scripts/calculators/ — one module per component; pure functions, fully unit-tested.
  • scripts/scorer.py — weighted composite with proportional weight redistribution.
  • scripts/tests/ — tests covering every component, the scorer, sparse-data fail-closed behavior, and end-to-end bull/bear/degraded snapshots.

Known Limitations

  • Dominance history accumulates locally. CoinGecko's free tier only exposes current dominance, so the client stores one observation per run-day in the cache dir. The dominance component reports data_available: false until 31 daily observations exist (weight is redistributed until then). Seed it faster via --input-json.
  • Funding is best-effort. If Binance's endpoint is unreachable (geo-restrictions, outage), the component is skipped gracefully.
  • Universe is top-N by market cap with stablecoins and wrapped/staked assets excluded; it is not a fixed index, so composition drifts with the market.
  • Thresholds are heuristic and documented in the methodology reference; they are conservative defaults, not backtested optima.

Disclaimer

Educational and process-improvement use only. This skill describes market conditions; it does not provide financial advice, signals, or buy/sell instructions. All decisions remain the user's responsibility.

Related skills

researchmattpocock575KInvestigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.paper-context-resolverlllllllama451KRigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing Renv-and-assets-bootstraplllllllama450KRigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.ai-research-explorelllllllama311KRigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow c

Search skills and MCP servers

Fuzzy search across 23,137 skills and servers