- Home
- Skills
- Dkyazzentwatwa
- Chatgpt Skills
- Crypto Ta Analyzer
crypto-ta-analyzer_skill
- Python
12
GitHub Stars
5
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill dkyazzentwatwa/chatgpt-skills --skill crypto-ta-analyzer- .DS_Store14.0 KB
- .gitignore404 B
- CLAUDE.md2.7 KB
- requirements.txt29 B
- SKILL.md13.3 KB
Overview
This skill performs multi-indicator technical analysis for cryptocurrencies and stocks using 29+ proven indicators (RSI, MACD, Bollinger Bands, Ichimoku, OBV, etc.). It combines divergence detection, volume confirmation, and Bollinger squeeze alerts to produce a clear 7-tier trading signal and confidence metrics. The analyzer is designed for trend identification, comparative screening, and multi-timeframe confirmation.
How this skill works
The analyzer accepts historical price (and optional volume) data, normalizes it to OHLCV format, then runs a weighted set of indicators to compute individual scores and signals. It detects divergences, checks volume confirmation, flags Bollinger squeezes, and aggregates results into a normalized score, confidence value, and a 7-tier trade signal (STRONG_BUY to STRONG_SELL). Outputs include per-indicator signals, regime metrics (ADX/DMI), and warnings.
When to use it
- Quickly assess a single coin or stock trend (7–30 day view) before trading decisions
- Compare multiple assets to rank relative strength and identify top opportunities
- Confirm entries/exits by checking multi-timeframe agreement and volume confirmation
- Monitor a watchlist for STRONG_BUY/STRONG_SELL signals in trending market regimes
- Detect potential breakouts early using Bollinger Band squeeze and rising volume
Best practices
- Validate and normalize data quality first; use 100+ data points (200+ preferred)
- Always view signals in market context—check regime (ADX/DMI) and recent news
- Use consensus across multiple indicators and timeframes instead of one signal
- Require volume confirmation for higher conviction; prioritize OBV and MFI checks
- Treat indicators as tools for probability, not guarantees; manage risk with stop-losses
Example use cases
- Run a 7-day analysis for Bitcoin to get a quick buy/neutral/sell view with confidence and volume confirmation
- Compare top 10 coins over 30 days to rank winners and identify sector leaders
- Scan a watchlist continuously to surface STRONG_BUY signals for trend-following entries
- Perform deep-dive 7/30/90 day analyses to confirm multi-timeframe agreement before allocating capital
- Detect potential breakouts by combining Bollinger squeeze alerts with rising ADX and volume
FAQ
Provide price history normalized to OHLCV. Volume is recommended. Use at least 100 data points; 200+ is better.
How should I interpret the 7-tier signal?
Signals combine normalized score and confidence: STRONG_BUY/SELL require high normalized magnitude and confidence; BUY/SELL are moderate; WEAK and NEUTRAL indicate low conviction.