Crypto Signal

D 52 completed

Other

unknown / python · tiny

Files

3,571

LOC

Frameworks

Languages

Overview Files & Metrics Git Activity Call Graph Security Reports

Pipeline State

completed

Run ID

#385504

Phase

done

Progress

Started

Finished

2026-04-13 01:31:02

LLM tokens

Pipeline Metadata

Stage

Skipped

Decision

skip_scaffold_dup

Novelty

32.75

Framework unique

—

Isolation

—

Last stage change

2026-04-16 18:15:42

Deduplication group #48145

Member of a group with 1 similar repo(s) — canonical #117145 view group →

Top concepts (6)

Monolithic ScriptArchitecture DescriptionProject DescriptionWeb BackendFactoryLogging

Repobility · severity-and-effort ranking · https://repobility.com

🧪 Code Distillation

AI Prompt

Create a real-time cryptocurrency trading signal analyzer using Python and Flask. I need it to analyze multiple trading pairs from Binance using a 5-rule confluence framework. The dashboard should display high-quality buy/sell signals, including a confidence score. For each signal, it must show a complete trade setup card with entry price, stop loss, three take-profit targets, suggested leverage, and calculated position size. Please also include features for a trade journal to log and track P/L, performance stats like win rate, and options for browser notifications and audio alerts when a signal fires.

python flask crypto trading binance technical-analysis real-time dashboard

Generated by gemma4:latest

Catalog Information

A real-time cryptocurrency trading signal analyzer using a 5-rule confluence framework. Analyzes multiple trading pairs on Binance and displays high-quality buy/sell signals based on technical indicators.

Description

Novelty

3/10

Technologies

flask

Claude Models

claude-opus-4-6

Quality Score

51.7/100

Structure

Code Quality

Documentation

Testing

Practices

Security

100

Dependencies

Strengths

Consistent naming conventions (snake_case)
Good security practices \u2014 no major issues detected

Weaknesses

No LICENSE file \u2014 legal ambiguity for contributors
No tests found \u2014 high risk of regressions
No CI/CD configuration \u2014 manual testing and deployment
350 duplicate lines detected \u2014 consider DRY refactoring
1 'god files' with >500 LOC need decomposition

Recommendations

Add a test suite \u2014 start with critical path integration tests
Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
Add a linter configuration to enforce code style consistency
Add a LICENSE file (MIT recommended for open source)

Security & Health

4.6h

Tech Debt (C)

Medium

DORA Rating

OWASP (100%)

PASS

Quality Gate

Risk (3)

Unknown

License

3.4%

Duplication

Full Security Report AI Fix Prompts SARIF SBOM

Languages

python

65.9%

markdown

32.7%

text

1.4%

Frameworks

Flask

Symbols

function28

constant9

variable6

class1

method1

API Endpoints (12)

Source: Repobility analyzer (https://repobility.com)
Method	Path	Handler	Framework
About: code-quality intelligence by Repobility · https://repobility.com
GET	`/`	index	FastAPI/Flask
GET	`/api/analyze`	api_analyze	FastAPI/Flask
GET	`/api/chart-data`	api_chart_data	FastAPI/Flask
GET	`/api/history`	api_history	FastAPI/Flask
GET	`/api/settings`	api_get_settings	FastAPI/Flask
POST	`/api/settings`	api_save_settings	FastAPI/Flask
GET	`/api/symbols`	api_symbols	FastAPI/Flask
GET	`/api/trades`	api_get_trades	FastAPI/Flask
POST	`/api/trades`	api_create_trade	FastAPI/Flask
GET	`/api/trade-setup`	api_trade_setup	FastAPI/Flask
PUT	`/api/trades/<int:trade_id>`	api_update_trade	FastAPI/Flask
GET	`/api/trades/stats`	api_trade_stats	FastAPI/Flask

Concepts (6)

Repobility · code-quality intelligence · https://repobility.com
Category	Name	Description	Confidence
All rows scored by the Repobility analyzer (https://repobility.com)
ai_arch_pattern	Monolithic Script	The entire application logic resides in a single Python module (`analyzer.py`) with no separation into layers such as data access, business logic, or presentation.	100%
ai_architecture	Architecture Description	# Architecture Overview: kennethsolomon__crypto-signal ## 1. Executive Summary `crypto-signal` is a lightweight Python service that fetches cryptocurrency market data, processes it to generate trading signals, and exposes the results via a Flask HTTP endpoint. It is intended for quick prototyping and personal use rather than production deployment. The architecture is a monolithic script style, with all business logic residing in `analyzer.py` and the web layer in `app.py`. Key strength: Rapid development and minimal dependencies make it easy to run locally. Key risk: Lack of separation of concerns, no testing, and no security controls expose the system to maintenance and operational issues. --- ## 2. System Architecture Diagram ```mermaid graph TD Client[HTTP Client] -->\|GET /analyze\| FlaskApp[app.py] FlaskApp -->\|calls\| Analyzer[analyzer.py] Analyzer -->\|HTTP GET\| ExternalAPI[External Crypto API] Analyzer -->\|JSON\| FlaskApp FlaskApp -->\|HTTP Response\| Client ``` --- ## 3. Architectural Layers \| Layer \| Responsibility \| Key files/directories \| Boundary enforcement \| Dependencies \| \|-------\|----------------\|-----------------------\|----------------------\|--------------\| \| Presentation \| Exposes HTTP endpoints, serializes responses \| `app.py` \| Very loose – the Flask route directly imports functions from `analyzer.py` \| `flask`, `analyzer.py` \| \| Application / Service \| Orchestrates signal generation, coordinates data fetching \| `analyzer.py` \| Entire business logic lives in a single file; no sub‑packages \| `requests`, `pandas`, `numpy` \| \| Domain \| Core signal‑generation algorithms (e.g., moving‑average crossover) \| `analyzer.py` \| Embedded in the same file as service logic \| None \| \| Infrastructure \| External API calls, logging, configuration \| `analyzer.py` (uses `requests`), `logging` module \| No dedicated infra layer; logging configured at module import \| `logging`, `requests` \| Boundary enforcement is minimal; the monolithic design couples all layers together. --- ## 4. Component Catalog \| Component \| Location \| Responsibility \| Public Interface \| Dependencies \| Dependents \| \|-----------\|----------\|----------------\|------------------\|--------------\|------------\| \| Flask App \| `app.py` \| Handles HTTP requests, returns JSON \| `app = Flask(__name__)`, `@app.route('/analyze')` \| `flask`, `analyzer` \| Client \| \| Analyzer \| `analyzer.py` \| Fetches market data, computes signals, returns dict \| `def analyze_market(symbol, timeframe)` \| `requests`, `pandas`, `numpy`, `logging` \| `app.py` \| \| Logging \| Standard `logging` module \| Records debug/info messages \| `logging.getLogger(__name__)` \| `logging` \| `analyzer.py` \| \| Configuration \| Hard‑coded in `analyzer.py` (API keys, URLs) \| Holds constants \| None \| None \| `analyzer.py` \| --- ## 5. Component Interactions The primary interaction flow is a synchronous HTTP request from a client to the Flask endpoint, which then calls the analyzer logic and returns a JSON payload. ```mermaid sequenceDiagram participant C as Client participant A as Flask App (app.py) participant N as Analyzer (analyzer.py) participant E as External API C->>A: GET /analyze?symbol=BTCUSDT&timeframe=1h A->>N: analyze_market(symbol, timeframe) N->>E: GET https://api.crypto.com/v1/klines?symbol=BTCUSDT&interval=1h E-->>N: JSON market data N-->>A: signal dict A-->>C: JSON response ``` --- ## 6. Data Flow \| Step \| Source \| Transformation \| Storage \| Destination \| \|------\|--------\|----------------\|---------\|-------------\| \| 1 \| HTTP request parameters (`symbol`, `timeframe`) \| Parsed by Flask route \| None \| `analyzer.analyze_market` \| \| 2 \| External Crypto API \| JSON → Pandas DataFrame → Signal calculation \| None \| `analyzer` \| \| 3 \| Signal dict \| Serialized to JSON \| None \| HTTP response to client \| No persistent storage is used; all data is transient. --- ## 7. Technology Decisions & Rationale \| Technology \| Choice \| Likely Rationale \| Alternatives \| Risks \| \|------------\|--------\|------------------\|--------------\|-------\| \| Python 3.9 \| General‑purpose, easy to write scripts \| Rapid prototyping \| Go, Rust, Node.js \| GIL limits concurrency \| \| Flask \| Lightweight web framework \| Minimal boilerplate \| FastAPI, Django \| No async support, limited scalability \| \| requests \| Simple HTTP client \| Mature, synchronous \| httpx (async), urllib3 \| Blocking I/O \| \| pandas / numpy \| Data manipulation \| Familiar to data scientists \| pure Python, Dask \| Memory overhead \| \| logging \| Standard library \| No external dependency \| loguru, structlog \| Basic formatting only \| --- ## 8. Scalability Considerations - Current bottlenecks: Single‑threaded Flask server; synchronous `requests` calls block the event loop. - Horizontal scaling: Possible by running multiple Gunicorn workers, but each worker will still block on I/O. - Stateful vs Stateless: Stateless – no session or database state. - Caching: None; repeated API calls for the same symbol/timeframe will hit the external service each time. --- ## 9. Security Considerations \| Area \| Observation \| Risk \| \|------\|-------------\|------\| \| Authentication \| None – any client can hit `/analyze`. \| Unauthorized usage, potential DoS. \| \| Input Validation \| Parameters are passed directly to `requests` without sanitization. \| Injection or malformed requests could cause errors. \| \| Secret Management \| API keys (if any) are hard‑coded or missing. \| Exposure if repository is public. \| \| Logging \| Uses default `logging` level; may leak sensitive data if not configured. \| Information leakage. \| --- ## 10. Testing Strategy Assessment \| Test Type \| Presence \| Framework \| Coverage \| Gaps \| \|-----------\|----------\|-----------\|----------\|------\| \| Unit \| None \| – \| 0% \| No tests for `analyzer` functions. \| \| Integration \| None \| – \| 0% \| No tests for Flask routes. \| \| End‑to‑End \| None \| – \| 0% \| No automated end‑to‑end tests. \| Evidence: Repository scan shows no files matching `test_.py` or `_test.py`. The `tasks` folder contains only markdown documentation, not code. --- ## 11. Technical Debt Assessment \| Category \| Description \| Severity \| Effort to Fix \| \|----------\|-------------\|----------\|---------------\| \| Monolithic Design \| All logic in `analyzer.py` and `app.py`. \| High \| Medium – refactor into packages. \| \| No Tests \| Zero automated tests. \| Critical \| High – add unit & integration tests. \| \| Hard‑coded Config \| API URLs and keys embedded in code. \| Medium \| Low – externalize to `.env` or config module. \| \| Missing Type Hints \| Functions lack `typing` annotations. \| Low \| Low – add hints. \| \| No Logging Configuration \| Default logger may leak data. \| Low \| Low – configure log format & level. \| \| No Security Controls \| Open endpoint, no auth. \| High \| Medium – add API key or token auth. \| --- ## 12. Recommendations \| Recommendation \| Rationale \| Implementation Steps \| \|----------------\|-----------\|----------------------\| \| Modularize \| Separate web, service, domain, infra into distinct packages. \| Create `crypto_signal/` package with sub‑modules (`app`, `analyzer`, `infra`). \| \| Add Tests \| Prevent regressions and document behavior. \| Write `tests/test_analyzer.py` and `tests/test_app.py` using `pytest`. \| \| Type Hints \| Improve readability & IDE support. \| Add `typing` annotations to all public functions. \| \| Configuration Layer \| Centralize constants & secrets. \| Create `config.py` or use `python-dotenv`. \| \| Async I/O \| Reduce blocking I/O. \| Switch to `httpx` or `aiohttp` and use `FastAPI` or async Flask extensions. \| \| Caching \| Reduce external API load. \| Implement simple in‑memory cache (`functools.lru_cache`) or Redis. \| \| Security \| Protect endpoint. \| Add token‑based auth (e.g., `flask-httpauth`) or API key validation. \| \| Logging \| Structured logs. \| Use `loguru` or `structlog` for better formatting. \| ---	85%
auto_description	Project Description	A real-time cryptocurrency trading signal analyzer using a 5-rule confluence framework. Analyzes multiple trading pairs on Binance and displays high-quality buy/sell signals based on technical indicators.	80%
auto_category	Web Backend	web-backend	70%
design_pattern	Factory	Found factory/create_ naming patterns	60%
business_logic	Logging	Detected from 3 related files	50%

Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/

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1 quality score recorded.

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BinComp Dependency Hardening

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3 of this repo's dependencies have been scanned for binary hardening. Grade reflects RELRO / stack canary / FORTIFY / PIE coverage.

Nflask3.1.3 · 0 gadgets · risk 574.2 Fnumpy2.4.4 · 6,596 gadgets · risk 0.0 Fpandas3.0.2 · 6,381 gadgets · risk 0.0

Crypto Signal

Pipeline State

Pipeline Metadata

🧪 Code Distillation

AI Prompt

Catalog Information

Description

Novelty

Tags

Technologies

Claude Models

Quality Score

Strengths

Weaknesses

Recommendations

Security & Health

Languages

Frameworks

Symbols

API Endpoints (12)

Concepts (6)

Quality Timeline

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BinComp Dependency Hardening