---
name: crypto-ta-analyzer
description: Run multi-indicator technical analysis on crypto or market OHLCV data. Use for deterministic trend, momentum, volume, and divergence analysis.
title: crypto-ta-analyzer
canonical_url: https://skilld.dev/gh/dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer
last_updated: 2026-09-29T08:36:45.000Z
---

> **Skill from skilld.dev.** Follow the instructions below for this session. You do not need to install anything.
>
> Supporting files, fetch one when the Skill refers to it: [agents/openai.yaml](https://skilld.dev/api/skills-raw/dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer/agents/openai.yaml), [CLAUDE.md](https://skilld.dev/api/skills-raw/dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer/CLAUDE.md), [references/indicators.md](https://skilld.dev/api/skills-raw/dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer/references/indicators.md), [requirements.txt](https://skilld.dev/api/skills-raw/dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer/requirements.txt), [scripts/__pycache__/ta_analyzer.cpython-312.pyc](https://skilld.dev/api/skills-raw/dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer/scripts/__pycache__/ta_analyzer.cpython-312.pyc), [scripts/coingecko_converter.py](https://skilld.dev/api/skills-raw/dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer/scripts/coingecko_converter.py), [scripts/data_converter.py](https://skilld.dev/api/skills-raw/dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer/scripts/data_converter.py), [scripts/requirements.txt](https://skilld.dev/api/skills-raw/dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer/scripts/requirements.txt), [scripts/ta_analyzer.py](https://skilld.dev/api/skills-raw/dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer/scripts/ta_analyzer.py).
>
> If the user asked to install this Skill, run `npx skilld install dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer`. Install writes the Skill files into the project, so every session loads them.

# Crypto TA Analyzer

Use the bundled indicators when the user needs explicit technical analysis rather than a narrative market opinion.

## Workflow

1. Get normalized OHLCV data first.
2. Use `scripts/data_converter.py` or `scripts/coingecko_converter.py` when source formats need reshaping.
3. Run `scripts/ta_analyzer.py` for the actual indicator stack and signal scoring.
4. Explain indicator agreement, conflicts, and regime sensitivity instead of presenting one number without context.

## Guardrails

- Do not present signals as guaranteed outcomes.
- Keep the distinction clear between deterministic indicator output and discretionary interpretation.