Noon Barbari Backtesting

Crypto backtesting tools: real backtests with robustness verdicts, daily signals and market data.

사용해야 할까요

품질 및 안전성

A
설명 품질
100%
스키마 완전성
85%
이름 품질
98%
오염 위험
100%
권한 일치
100%
프로토콜 준수
100%

도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.

컨텍스트 비용

~1,720토큰 (도구 정의)
~799 B일반적인 응답 크기
중간 정도의 주의 영향 (128k 컨텍스트의 1.34%)

이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.

설치

원클릭 설치

`claude_desktop_config.json` 파일에 다음을 추가하세요:

{
  "mcpServers": {
    "backtesting": {
      "url": "https://noonbarbari.xyz/mcp"
    }
  }
}

원격 엔드포인트

https://noonbarbari.xyz/mcpstreamable-http

할 수 있는 일

도구 목록

도구 (11)

🟢 읽기 전용🟡 쓰기🔴 삭제⚪ 알 수 없음
🟢list_strategies

List the strategy templates available for backtesting and comparison (name, title, one-line description). Use the returned `name` value as the strategy identifier in other tools.

입력 스키마

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_coin_signals(coin)

Today's daily-bar indicator readings for crypto coins, computed by a real backtesting engine from Binance closes: price, RSI-14, MACD state, SMA 50/200 posture, SuperTrend, Bollinger position, ATR volatility, 52-week range. Pass a coin ticker (e.g. 'btc') for one coin, or omit for the whole 50-coin board.

입력 스키마

{
  "type": "object",
  "properties": {
    "coin": {
      "type": "string",
      "description": "Lower-case ticker, e.g. btc, eth, sol. Omit for all coins."
    }
  },
  "additionalProperties": false
}
🟢get_buy_hold(coin)

What a $1,000 buy of a coin on Jan 1 of each available year would be worth today — ROI, peak value and date, and the maximum drawdown endured along the way. Real Binance data, refreshed daily.

입력 스키마

{
  "type": "object",
  "properties": {
    "coin": {
      "type": "string",
      "description": "Lower-case ticker, e.g. btc, eth, sol."
    }
  },
  "required": [
    "coin"
  ],
  "additionalProperties": false
}
🟢get_overfitting_index

The Crypto Overfitting Index: the monthly share (%) of default-parameter strategy configurations (10 templates × 50 coins) whose out-of-sample Sharpe turned negative — how much of what backtests promise fails on unseen data. Returns the current reading and full history.

입력 스키마

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢compare_strategies(strategy_a, strategy_b)

Head-to-head comparison of two strategy templates from real monthly engine runs across ~50 coins: per-coin win count, median out-of-sample Sharpe, survival counts, median return and drawdown. Use strategy names from list_strategies (e.g. 'super_trend', 'ema_crossover').

입력 스키마

{
  "type": "object",
  "properties": {
    "strategy_a": {
      "type": "string",
      "description": "First strategy name, e.g. super_trend"
    },
    "strategy_b": {
      "type": "string",
      "description": "Second strategy name, e.g. ema_crossover"
    }
  },
  "required": [
    "strategy_a",
    "strategy_b"
  ],
  "additionalProperties": false
}
🟢search_answers(query)

Search Noon Barbari's Q&A knowledge base of direct, data-grounded answers about backtesting, overfitting, validation, indicators, risk management and crypto markets. Returns the top matching questions with their full answers.

입력 스키마

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Free-text query, e.g. 'why do backtests fail'"
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false
}
⚪run_backtest(strategy, start_date, starting_cash)

Run a real backtest of a strategy template on BTC/USDT from a start date (public what-if engine; may take up to a minute on a cache miss; rate-limited). Returns net return, max drawdown, trade count, a robustness score with an overfitting verdict, and a shareable result URL.

입력 스키마

{
  "type": "object",
  "properties": {
    "strategy": {
      "type": "string",
      "description": "Strategy name from list_strategies, e.g. super_trend"
    },
    "start_date": {
      "type": "string",
      "description": "ISO date, e.g. 2022-01-01 (2020-01-01 or later)"
    },
    "starting_cash": {
      "type": "number",
      "description": "Starting balance in USD (default 10000, max 1000000)"
    }
  },
  "required": [
    "strategy",
    "start_date"
  ],
  "additionalProperties": false
}
🟢get_dca(coin, amount, frequency, start_date)

Dollar-cost-averaging outcome for a coin: what buying a fixed dollar amount on a schedule (weekly or monthly) since a start date would be worth today — total invested, units, average cost, current value and ROI — plus the lump-sum comparison and the worst drawdown endured. Real Binance closes, refreshed daily.

입력 스키마

{
  "type": "object",
  "properties": {
    "coin": {
      "type": "string",
      "description": "Lower-case ticker, e.g. btc, eth, sol."
    },
    "amount": {
      "type": "number",
      "description": "USD invested per purchase (default 100)."
    },
    "frequency": {
      "type": "string",
      "enum": [
        "weekly",
        "monthly"
      ],
      "description": "Purchase cadence (default weekly)."
    },
    "start_date": {
      "type": "string",
      "description": "ISO date to start buying from, e.g. 2021-01-01 (optional; default = full history)."
    }
  },
  "required": [
    "coin"
  ],
  "additionalProperties": false
}
🟢check_overfitting(sharpe, timeframe, length_days, n_trials, skew, ...)

Compute the Deflated Sharpe Ratio (Bailey & Lopez de Prado 2014) for YOUR OWN backtest: given its annualised Sharpe, length, and how many strategy variants you tried before selecting it, returns the probability the result is real skill rather than selection luck, the luck bar it must clear, and a plain verdict. Works on any backtest, not just ours.

입력 스키마

{
  "type": "object",
  "properties": {
    "sharpe": {
      "type": "number",
      "description": "Annualised Sharpe ratio of the selected backtest."
    },
    "timeframe": {
      "type": "string",
      "enum": [
        "1h",
        "4h",
        "1d",
        "1w"
      ],
      "description": "Bar timeframe of the returns (default 1d)."
    },
    "length_days": {
      "type": "number",
      "description": "Length of the backtest in calendar days."
    },
    "n_trials": {
      "type": "number",
      "description": "How many strategy/parameter variants were tried before picking this one."
    },
    "skew": {
      "type": "number",
      "description": "Skewness of the return series (default 0)."
    },
    "kurtosis": {
      "type": "number",
      "description": "Non-excess kurtosis of returns (Gaussian = 3, the default)."
    }
  },
  "required": [
    "sharpe",
    "length_days",
    "n_trials"
  ],
  "additionalProperties": false
}
🟢query_dataset(template, coin)

Query our open 11,440-run curve-fitting study (10 strategy templates x 20 coins, 70/30 in-sample/out-of-sample split). Returns the headline overfitting stats and the tuned picks matching an optional template and/or coin filter — each with in-sample vs out-of-sample Sharpe, the Sharpe haircut, and the in-sample-to-out-of-sample parameter rank correlation.

입력 스키마

{
  "type": "object",
  "properties": {
    "template": {
      "type": "string",
      "description": "Strategy name, e.g. super_trend (optional). Omit for all templates."
    },
    "coin": {
      "type": "string",
      "description": "Ticker, e.g. btc (optional). Omit for all coins."
    }
  },
  "additionalProperties": false
}
🟢search_glossary(query)

Search Noon Barbari's trading glossary for a plain-language definition of an indicator, metric or concept (RSI, MACD, Sharpe ratio, drawdown, walk-forward, overfitting, and 60+ more). Returns the top matching terms with a short definition, the full explanation, and a link.

입력 스키마

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "A term or question, e.g. 'deflated sharpe' or 'what is RSI'."
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false
}

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