Mean Reversion Strategy

Mean Reversion Strategy MCP Connector for Claude

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Identify price extremes and mean-reversion entry points using Z-score and RSI filters.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides quantitative tools to identify statistical price extremes. By using calculate_z_score_signals, agents can detect BUY and SELL opportunities when prices deviate significantly from their moving average, confirmed by RSI momentum filters. Additionally, use get_reversion_probability to estimate the likelihood of a price returning to its mean, or get_strategy_summary to analyze price distribution and volatility.

tradingz-scorersimean-reversionquantitative

3 tools expose this connector's capabilities to your AI agent.

calculate_z_score_signals

get_reversion_probability

Calculates historical probability of price returning to mean

get_strategy_summary

Provides statistical overview of price distribution and volatility

See how to talk to your AI agent using Mean Reversion Strategy.

Analyze these closing prices for mean-reversion signals: [150.2, 151.5, 149.8, 148.5, 147.0, 145.2, 144.0, 146.5]

BUY signal detected at price 144.0. Z-score is -2.45 and RSI is 28.0. Take-profit target is 148.5.

What is the probability of price returning to the mean if the Z-score is -2.5?

Based on the historical data provided, there is a 68% probability that the price will return to the mean from a Z-score of -2.5.

Give me a statistical summary of these prices: [100, 102, 98, 101, 99, 105, 95]

The mean price is 100.0, the standard deviation is 3.16, and the volatility coefficient is 0.0316.

Signals are generated when the Z-score exceeds the specified threshold and the RSI confirms the momentum direction (oversold for BUY, overbought for SELL).

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