What is Crypto Indicators Mcp

crypto-indicators-mcp is an MCP server that provides a variety of technical analysis indicators and strategies for cryptocurrency trading, enabling AI trading agents to analyze market trends and create effective quantitative strategies.

Use cases

Use cases for crypto-indicators-mcp include developing automated trading bots, conducting market trend analysis, and implementing quantitative trading strategies across different cryptocurrency exchanges.

How to use

To use crypto-indicators-mcp, clone the repository from GitHub, install the necessary dependencies using npm, and configure the MCP client to connect to the server by specifying the command and environment variables in the config file.

Key features

Key features include over 50 technical indicators categorized by trend, momentum, volatility, and volume, trading strategies that generate buy/sell signals, flexible data source options, and a modular design for easy maintenance.

Where to use

crypto-indicators-mcp can be used in various fields related to cryptocurrency trading, including algorithmic trading, market analysis, and financial technology applications.

Crypto Indicators MCP Server

An MCP server providing a range of cryptocurrency technical analysis indicators and strategies, empowering AI trading agents to efficiently analyze market trends and develop robust quantitative strategies.

Features

• Technical Indicators: 50+ indicators across trend, momentum, volatility, and volume categories.
• Trading Strategies: Corresponding strategies outputting signals: -1 (SELL), 0 (HOLD), 1 (BUY).
• Flexible Data Source: Defaults to Binance, configurable to any ccxt-supported exchange.
• Modular Design: Indicators and strategies are categorized for easy maintenance.

Installation

Prerequisites

• Node.js (v18.x or higher)
• npm (v8.x or higher)

Steps

1. Clone the Repository:

   git clone https://github.com/kukapay/crypto-indicators-mcp.git
   cd crypto-indicators-mcp
   

2. Install Dependencies:

   npm install
   

3. Configure MCP Client:
   To use this server with an MCP client like Claude Desktop, add the following to your config file (or equivalent):

   {
     "mcpServers": {
       "crypto-indicators-mcp": {
         "command": "node",
         "args": [
           "path/to/crypto-indicators-mcp/index.js"
         ],
         "env": {
           "EXCHANGE_NAME": "binance"
         }
       }
     }
   }
   

Available Tools

Trend Indicators

• calculate_absolute_price_oscillator: Measures the difference between two EMAs to identify trend strength (APO).
• calculate_aroon: Identifies trend changes and strength using high/low price extremes (Aroon).
• calculate_balance_of_power: Gauges buying vs. selling pressure based on price movement (BOP).
• calculate_chande_forecast_oscillator: Predicts future price movements relative to past trends (CFO).
• calculate_commodity_channel_index: Detects overbought/oversold conditions and trend reversals (CCI).
• calculate_double_exponential_moving_average: Smooths price data with reduced lag for trend detection (DEMA).
• calculate_exponential_moving_average: Weights recent prices more heavily for trend analysis (EMA).
• calculate_mass_index: Identifies potential reversals by measuring range expansion (MI).
• calculate_moving_average_convergence_divergence: Tracks momentum and trend direction via EMA differences (MACD).
• calculate_moving_max: Computes the maximum price over a rolling period (MMAX).
• calculate_moving_min: Computes the minimum price over a rolling period (MMIN).
• calculate_moving_sum: Calculates the sum of prices over a rolling period (MSUM).
• calculate_parabolic_sar: Provides stop-and-reverse points for trend following (PSAR).
• calculate_qstick: Measures buying/selling pressure based on open-close differences (Qstick).
• calculate_kdj: Combines stochastic and momentum signals for trend analysis (KDJ).
• calculate_rolling_moving_average: Applies a rolling EMA for smoother trend tracking (RMA).
• calculate_simple_moving_average: Averages prices over a period to identify trends (SMA).
• calculate_since_change: Tracks the time since the last significant price change.
• calculate_triple_exponential_moving_average: Reduces lag further than DEMA for trend clarity (TEMA).
• calculate_triangular_moving_average: Weights middle prices more for smoother trends (TRIMA).
• calculate_triple_exponential_average: Measures momentum with triple smoothing (TRIX).
• calculate_typical_price: Averages high, low, and close prices for a balanced trend view.
• calculate_volume_weighted_moving_average: Incorporates volume into moving averages for trend strength (VWMA).
• calculate_vortex: Identifies trend direction and strength using true range (Vortex).

Momentum Indicators

• calculate_awesome_oscillator: Measures market momentum using midline crossovers (AO).
• calculate_chaikin_oscillator: Tracks accumulation/distribution momentum (CMO).
• calculate_ichimoku_cloud: Provides a comprehensive view of support, resistance, and momentum (Ichimoku).
• calculate_percentage_price_oscillator: Normalizes MACD as a percentage for momentum (PPO).
• calculate_percentage_volume_oscillator: Measures volume momentum via EMA differences (PVO).
• calculate_price_rate_of_change: Tracks price momentum as a percentage change (ROC).
• calculate_relative_strength_index: Identifies overbought/oversold conditions via momentum (RSI).
• calculate_stochastic_oscillator: Compares closing prices to ranges for momentum signals (STOCH).
• calculate_williams_r: Measures momentum relative to recent high-low ranges (Williams %R).

Volatility Indicators

• calculate_acceleration_bands: Frames price action with dynamic volatility bands (AB).
• calculate_average_true_range: Measures market volatility based on price ranges (ATR).
• calculate_bollinger_bands: Encloses price action with volatility-based bands (BB).
• calculate_bollinger_bands_width: Quantifies volatility via band width changes (BBW).
• calculate_chandelier_exit: Sets trailing stop-losses based on volatility (CE).
• calculate_donchian_channel: Tracks volatility with high/low price channels (DC).
• calculate_keltner_channel: Combines ATR and EMA for volatility bands (KC).
• calculate_moving_standard_deviation: Measures price deviation for volatility (MSTD).
• calculate_projection_oscillator: Assesses volatility relative to projected prices (PO).
• calculate_true_range: Calculates daily price range for volatility analysis (TR).
• calculate_ulcer_index: Quantifies downside volatility and drawdowns (UI).

Volume Indicators

• calculate_accumulation_distribution: Tracks volume flow to confirm price trends (AD).
• calculate_chaikin_money_flow: Measures buying/selling pressure with volume (CMF).
• calculate_ease_of_movement: Assesses how easily prices move with volume (EMV).
• calculate_force_index: Combines price and volume for momentum strength (FI).
• calculate_money_flow_index: Identifies overbought/oversold via price-volume (MFI).
• calculate_negative_volume_index: Tracks price changes on lower volume days (NVI).
• calculate_on_balance_volume: Accumulates volume to predict price movements (OBV).
• calculate_volume_price_trend: Combines volume and price for trend confirmation (VPT).
• calculate_volume_weighted_average_price: Averages prices weighted by volume (VWAP).

Trend Strategies

• calculate_absolute_price_oscillator_strategy: Generates buy/sell signals from APO crossovers (APO Strategy).
• calculate_aroon_strategy: Signals trend reversals using Aroon crossovers (Aroon Strategy).
• calculate_balance_of_power_strategy: Issues signals based on BOP thresholds (BOP Strategy).
• calculate_chande_forecast_oscillator_strategy: Predicts reversals with CFO signals (CFO Strategy).
• calculate_kdj_strategy: Combines KDJ lines for trend-based signals (KDJ Strategy).
• calculate_macd_strategy: Uses MACD crossovers for trading signals (MACD Strategy).
• calculate_parabolic_sar_strategy: Signals trend direction with PSAR shifts (PSAR Strategy).
• calculate_typical_price_strategy: Generates signals from typical price trends.
• calculate_volume_weighted_moving_average_strategy: Issues signals based on VWMA crossovers (VWMA Strategy).
• calculate_vortex_strategy: Signals trend direction with Vortex crossovers (Vortex Strategy).

Momentum Strategies

• calculate_momentum_strategy: Issues signals based on momentum direction.
• calculate_awesome_oscillator_strategy: Signals momentum shifts with AO crossovers (AO Strategy).
• calculate_ichimoku_cloud_strategy: Generates signals from Ichimoku cloud positions (Ichimoku Strategy).
• calculate_rsi2_strategy: Signals overbought/oversold with RSI thresholds (RSI Strategy).
• calculate_stochastic_oscillator_strategy: Uses stochastic crossovers for signals (STOCH Strategy).
• calculate_williams_r_strategy: Signals momentum reversals with Williams %R (Williams %R Strategy).

Volatility Strategies

• calculate_acceleration_bands_strategy: Signals breakouts with acceleration bands (AB Strategy).
• calculate_bollinger_bands_strategy: Issues signals from Bollinger Band breaches (BB Strategy).
• calculate_projection_oscillator_strategy: Signals volatility shifts with PO (PO Strategy).

Volume Strategies

• calculate_chaikin_money_flow_strategy: Signals volume pressure with CMF (CMF Strategy).
• calculate_ease_of_movement_strategy: Issues signals based on EMV trends (EMV Strategy).
• calculate_force_index_strategy: Signals momentum with force index shifts (FI Strategy).
• calculate_money_flow_index_strategy: Signals overbought/oversold with MFI (MFI Strategy).
• calculate_negative_volume_index_strategy: Signals trends with NVI changes (NVI Strategy).
• calculate_volume_weighted_average_price_strategy: Issues signals from VWAP crossovers (VWAP Strategy).

Usage Examples

Example 1: Calculate MACD Indicator

Input (Natural Language Prompt):

Calculate the MACD for BTC/USDT on a 1-hour timeframe with fast period 12, slow period 26, signal period 9, and fetch 100 data points.

Output:

{"macd": [...], "signal": [...], "histogram": [...]}

Example 2: Calculate RSI Strategy

Input (Natural Language Prompt):

Give me the RSI strategy signals for ETH/USDT on a 4-hour timeframe with a period of 14 and 50 data points.

Output:

[-1, 0, 1, 0, ...]

License

This project is licensed under the MIT License - see the LICENSE file for details.