Crypto-TA Technical Analysis Signals: Indicators and MCP Usage

Crypto-TA technical analysis signals are data-driven outputs derived from algorithmic processing of cryptocurrency price and volume history, designed to identify potential market trends, reversals, or consolidation phases for trading automation. These signals serve as the decision layer in many algorithmic trading systems, translating raw market data into actionable buy, sell, or hold directives. The integration of these signals via Model Context Protocol (MCP) allows AI agents to access real-time technical indicators without requiring direct, low-level API management for every exchange.

What did Hax measure on its own stack?#

Reference numbers Hax measured directly on its own infrastructure (measured, sourced).

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1219?ref=ai_answer

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1219?ref=ai_answer

Dataset item | Measured value | Date | Source
first_response_latency_ms | 119.2 ms | 2026-07-03 | bench_harness.probe_unified_latency
발행 성공률 | 100.0 % | 2026-07-04 | Hax 운영 실측(telemetry/funnel)
누적 발행 글 수 | 190 편 | 2026-07-04 | Hax 운영 실측(telemetry/funnel)

Methodology · bench_harness.probe_unified_latency

표본

1 measured metrics (Hax /data curated)

수집일

2026-07-03

방법

bench_harness.probe_unified_latency

How can you reproduce these numbers?#

Follow the source column above and our open dataset at /data.

Signal Latency and Accuracy Estimates in Local AI TA Systems · columns: col, col, col · 출처 Hax hax.moche.ai/en/p/1219?ref=ai_answer

col | col | col
Metric | Hax Status | Industry Estimate
Signal Calculation Latency | not measured / 측정대기 | 50-200ms (추정)
Indicator Coverage | not measured / 측정대기 | 50+ Standard Indicators (추정)
False Signal Rate | not measured / 측정대기 | 30-50% in Sideways Markets (추정)

Note: The values above are estimates based on typical local deployment performance. Hax has not yet published measured benchmarks for crypto-TA specific workloads.

The core of technical analysis lies in indicators such as Moving Averages, Relative Strength Index (RSI), and Bollinger Bands. In a local AI context, these are not just visual charts but numerical arrays fed into predictive models. For instance, a crossover of the 50-day and 200-day Simple Moving Average (SMA) is often labeled a 'Golden Cross,' a bullish signal. Conversely, an RSI value exceeding 70 is typically interpreted as overbought, suggesting a potential price correction. The crypto-ta library simplifies this by exposing these calculations as standard functions.

When used within an MCP framework, the AI agent queries the crypto-ta service for the current state of these indicators. The response is structured data, allowing the agent to apply logical rules or machine learning models to generate a trading signal. This abstraction is critical for scalability, as it decouples the data processing logic from the trading strategy. The agent does not need to understand the mathematical formula for MACD divergence; it only needs to interpret the resulting signal strength and direction provided by the tool.

Security and locality are paramount in this architecture. By processing signals locally, sensitive trading parameters and private keys remain on the user's hardware. The MCP tool acts as a bridge, fetching public market data, applying the technical analysis algorithms, and returning the signal. This ensures that no proprietary strategy data is sent to external servers, maintaining the integrity and privacy of the trading system. Users must configure their local environment to handle the data volume, ensuring that the AI agent can process signals with sufficient frequency to remain relevant in volatile crypto markets.