CryptoScholar

> Crypto technical analysis, directly inside Claude. CryptoScholar is a Model Context Protocol (MCP) server that gives Claude real-time TA capabilities — no chart-switching, no copy-pasting data, no context loss.

Ask Claude *"Is SOL set up for a swing trade?"* and it fetches live data from Binance, runs a full indicator suite, scores it, and delivers a grounded bull/bear debate — all in one response.

What it does

CryptoScholar exposes 15 MCP tools that Claude can call natively:

`analyze_coin`

Full technical analysis snapshot for any coin. Fetches 300 days of real OHLCV candles from Binance (with CoinGecko fallback) and computes:

`rank_coins`

Pass a list of symbols and get them back ranked by TSS. Runs in parallel (up to 8 workers) for fast results on large lists. Each result includes TSS, regime, EMA alignment, 4H MTF alignment, RSI divergence, OBV trend, funding rate, RSI-14, ADX-14, and RS vs BTC.

`top_coins`

No symbol list needed. Fetches the top 50 coins by market cap from CoinGecko and returns them ranked by TSS. Smart filtering automatically removes:

• Stablecoins (USDT, USDC, DAI, etc.)
• Wrapped / synthetic tokens (WBTC, WETH, stETH, cbBTC, etc.)
• Low-liquidity coins with 0.85), and uncorrelated pairs (

Ranking BTC, ETH, and XRP by Trend Strength Score — then drilling into the bear case for XRP

`rank_coins` scores each coin across trend, momentum, and relative strength vs BTC and returns them sorted by TSS. Here BTC leads at 63.7, ETH at 53.0, and XRP trails at 47.8 — all in `low_vol` regime. Asking for the XRP bear case immediately after surfaces the specific technical reasons: a steepest weekly EMA slope, faltering MACD, and ETH underperformance vs BTC flagged as early institutional exit pressure.

Full technical analysis snapshot for SOL — indicators, scoring, and bear case in one response

`analyze_coin` returns a structured breakdown covering EMA stack alignment, RSI, MACD, ADX, ATR, Bollinger Band width, OBV trend, funding rate, and relative strength vs BTC — all computed from 300 days of live Binance candles. Claude then reads the raw indicator values to generate a grounded bear case: EMA-200 resistance, weekly slope steepening, and MACD crossdown risk. No chart-switching, no copy-pasting — the full TA context is already in Claude's window.

Example output

`market_context()`

{
  "btc_price_30d_change_pct": -8.4,
  "btc_dominance_current": 54.2,
  "btc_dominance_30d_change_pct": 2.1,
  "eth_btc_20d_change_pct": -5.3,
  "total3_30d_change_pct": -14.6,
  "stablecoin_supply_usd": 196500000000,
  "stablecoin_30d_change_pct": 2.8,
  "fear_greed_value": 22,
  "fear_greed_label": "Fear",
  "btc_trend_score": 35.0,
  "ars": 28.5,
  "stablecoin_score": 60.0,
  "fear_greed_modifier": 0.0,
  "mrs": 42.3
}

`analyze_coin("SOL")`

{
  "symbol": "SOL",
  "data_source": "binance",
  "price": 142.30,
  "tss": 79.2,
  "regime": "mid_vol",
  "regime_source": "hmm",
  "vrs": 55,
  "ema_alignment": "full_bull",
  "mtf_alignment_4h": "bullish",
  "rsi_divergence": "none",
  "obv_trend": "rising",
  "funding_rate": 0.00012,
  "indicators": {
    "rsi_14": 61.4,
    "macd_hist": 0.42,
    "adx_14": 28.1,
    "atr_14": 6.82,
    "hv_20": 68.4,
    "rs_btc": 4.2,
    "bb_width": 0.18,
    "rsi_divergence": "none",
    "obv_trend": "rising"
  }
}

`correlate_coins(["BTC", "ETH", "SOL", "BNB"])`

{
  "symbols": ["BTC", "ETH", "SOL", "BNB"],
  "lookback_days": 30,
  "matrix": {
    "BTC": {"BTC": 1.0, "ETH": 0.91, "SOL": 0.78, "BNB": 0.83},
    "ETH": {"BTC": 0.91, "ETH": 1.0, "SOL": 0.82, "BNB": 0.79},
    "SOL": {"BTC": 0.78, "ETH": 0.82, "SOL": 1.0, "BNB": 0.71},
    "BNB": {"BTC": 0.83, "ETH": 0.79, "SOL": 0.71, "BNB": 1.0}
  },
  "high_correlation_pairs": [
    {"symbol_a": "BTC", "symbol_b": "ETH", "correlation": 0.91}
  ],
  "uncorrelated_pairs": []
}

`debate("SOL")`

{
  "bull_case": "SOL is in a full bullish EMA stack with RSI at 61 — healthy momentum without overbought conditions. ADX at 28 confirms trending structure, and relative strength vs BTC is positive at +4.2%, signalling capital rotation into SOL. Rising OBV confirms volume is flowing in on up-days.",
  "bear_case": "Historical volatility at 68% is elevated, and Bollinger Band width is widening — conditions that often precede sharp reversals. A break below EMA-20 would invalidate the current trend structure. Funding rate at 0.012% hints at building long leverage.",
  "bottom_line": "Technicals are constructive for continuation but volatility is high; position sizing should reflect the risk."
}

Configuration

Supported coins

CryptoScholar works with any coin listed on CoinGecko or Binance — just pass the ticker symbol. No configuration needed.

A built-in symbol map covers 65 major coins for instant resolution — the full top-50 market cap universe including BTC, ETH, SOL, BNB, XRP, ADA, AVAX, DOGE, LINK, DOT, SUI, TIA, WIF, BONK, and more. For anything outside that list, CryptoScholar automatically queries CoinGecko's search API to resolve the symbol and falls back to CoinGecko OHLCV if the coin isn't available on Binance.

In practice: if it trades somewhere and has a CoinGecko listing, it will work.

Architecture

Stateless by design — no database, no scheduler. Every tool call fetches fresh data.

Claude (MCP call)
    └── server.py              FastMCP entry point
         ├── tools/
         │    ├── analyze.py        Orchestrates fetch → indicators → regime → score
         │    ├── rank.py           Runs analyze_coin in parallel, sorts by TSS
         │    ├── top_coins.py      Fetches top N by market cap, delegates to rank_coins
         │    ├── correlate.py      Pairwise Pearson correlation of 30-day returns
         │    ├── watchlist.py      Watchlist + alert tools (7 tools)
         │    ├── debate.py         Builds prompt from TA data, calls Claude API
         │    └── market_context.py ARS + MRS + macro signals
         ├── ta/
         │    ├── indicators.py     pandas-ta + custom HV / RS / OBV functions
         │    ├── scoring.py        TSS: trend + momentum + RS ± MTF ± OBV bonuses
         │    ├── regime.py         HMM-first regime classifier with rule-based fallback
         │    └── hmm_regime.py     GaussianHMM train / persist / classify / auto-retrain
         ├── market/
         │    └── context.py        BTC dominance, ETH/BTC, TOTAL3, F&G, ARS, MRS
         └── data/
              ├── binance.py        Binance klines + funding rate (1,200 req/min, no auth)
              ├── coingecko.py      CoinGecko client, 5-min TTL cache, OHLCV builder
              ├── alternative_me.py Fear & Greed Index (Alternative.me, 1-hr cache)
              ├── defillama.py      DefiLlama stablecoin supply history
              └── watchlist_db.py   SQLite watchlist + alert persistence (~/.cryptoscholar/)

Data flow for `analyze_coin("SOL")`:

1. Map symbol → CoinGecko ID (`SOL` → `solana`)

2. Fetch 300-day daily OHLCV from Binance (`SOLUSDT` klines); fall back to CoinGecko if unavailable

3. Fetch 200-bar 4H OHLCV from Binance for multi-timeframe analysis

4. Fetch USDT-M perpetual funding rate from Binance Futures (null if no perpetual)

5. Compute all daily indicators via pandas-ta (EMA, RSI, MACD, ADX, ATR, BB, HV, OBV, RS vs BTC)

6. Compute OBV trend (EMA-10 of OBV slope over last 5 bars)

7. Compute 4H indicators (EMA-20/50) and derive MTF alignment bonus (±3 TSS pts)

8. Detect RSI divergence over last 30 bars (bullish/bearish/none)

9. Classify regime via GaussianHMM (hv_20 + normalised ATR + BBW); falls back to rule-based if no model

10. Compute TSS (weighted composite of trend, momentum, RS vs BTC ± MTF bonus ± OBV bonus)

11. Fetch current market data (price, market cap, 24h change) from CoinGecko

12. Return structured dict to Claude

Data flow for `market_context()`:

1. Fetch total market cap history (30d) from CoinGecko `/global/market_cap_chart`

2. Fetch BTC and ETH market chart history (30d) from CoinGecko

3. Fetch stablecoin supply history from DefiLlama

4. Fetch Fear & Greed Index from Alternative.me (1-hr cache)

5. Compute BTC dominance trend, ETH/BTC ratio trend, TOTAL3 change

6. Score into ARS (altcoin rotation) and MRS (market readiness + F&G modifier)

Development

make test            # run test suite
make test-parallel   # run tests in parallel (pytest-xdist)
make coverage        # coverage report
make lint-security   # bandit security scan

222 tests, 0 failures.

Roadmap

See ROADMAP.md for planned versions. Highlights:

• v0.8 — `research_coin` tool: web search + Jina reader for news and narrative context
• v0.9 — Market structure classification (HH/HL/LH/LL) via swing point detection; new `market_structure` field in `analyze_coin`
• v1.0 — Support & resistance zones clustered from swing pivots; `support_zones` + `resistance_zones` in `analyze_coin`
• v1.1 — Setup confluence score (1–5) measuring signal alignment; surfaces in `analyze_coin`, `rank_coins`, `watchlist_scan`
• v1.2 — Trade plan block: `entry_zone`, `take_profit`, `stop_loss`, `risk_reward_ratio` computed from S/R zones + ATR
• v1.3 — Pi Cycle indicator in `market_context`; `brief` tool: Claude Haiku one-paragraph setup summary
• v1.4 — EV filter: `ev_score` + `ev_signal` flags negative-EV setups before acting on a trade plan
• v1.5 — `backtest_strategy` tool: walk-forward simulation, fee-adjusted R, win rate, max drawdown

License

MIT