扫描报告
5 /100
tradingview-screener
Screen markets across 6 asset classes using TradingView data
A clean, legitimate market data screening tool with no security concerns. All capabilities match documentation, expression sandboxing is properly implemented, and no sensitive operations are performed.
可以安装
This skill is safe to use. No action required.
安全发现 1 项
| 严重性 | 安全发现 | 位置 |
|---|---|---|
| 低危 | Expression validation uses keyword blocklist 敏感访问 | scripts/signal_types.py:22 |
| 资源类型 | 声明权限 | 推断权限 | 状态 | 证据 |
|---|---|---|---|---|
| 文件系统 | NONE | READ | ✓ 一致 | Reads YAML config files only |
| 网络访问 | NONE | READ | ✓ 一致 | tvscreener library makes public API calls |
| 命令执行 | NONE | NONE | — | No shell execution found |
| 环境变量 | NONE | NONE | — | No environment variable access |
目录结构
19 文件 · 59.6 KB · 2145 行 Markdown 5f · 1032L
Python 7f · 881L
YAML 4f · 166L
Shell 1f · 51L
JSON 1f · 11L
Text 1f · 4L
├─
▾
assets
│ └─
signal-template.yaml
YAML
├─
▾
references
│ ├─
computed-signals-guide.md
Markdown
│ ├─
field-presets.md
Markdown
│ ├─
strategy-templates.md
Markdown
│ └─
tvscreener-api-guide.md
Markdown
├─
▾
scripts
│ ├─
▾
tests
│ │ ├─
__init__.py
Python
│ │ ├─
test_screen.py
Python
│ │ └─
test_signal_engine.py
Python
│ ├─
requirements.txt
Text
│ ├─
screen.py
Python
│ ├─
screener_constants.py
Python
│ ├─
signal_engine.py
Python
│ └─
signal_types.py
Python
├─
▾
state
│ └─
▾
signals
│ ├─
golden-cross.yaml
YAML
│ ├─
oversold-bounce.yaml
YAML
│ └─
volume-breakout.yaml
YAML
├─
_meta.json
JSON
├─
install.sh
Shell
└─
SKILL.md
Markdown
依赖分析 4 项
| 包名 | 版本 | 来源 | 已知漏洞 | 备注 |
|---|---|---|---|---|
tvscreener | >=0.2.0 | pip | 否 | Public TradingView API wrapper |
pandas | >=2.0.0 | pip | 否 | Standard data analysis library |
pyyaml | >=6.0 | pip | 否 | Uses yaml.safe_load for YAML parsing |
pytest | >=7.0.0 | pip | 否 | Development dependency only |
安全亮点
✓ No credential harvesting or environment variable access
✓ No shell execution or subprocess calls
✓ No file writes to sensitive paths
✓ No base64 encoding or obfuscation
✓ Expression validation with blocked keywords and df.eval() sandboxing
✓ Clean, documented market data operations only
✓ No external data exfiltration
✓ Proper test coverage validates behavior