Finml Toolkit
📖 技能介绍
version: "1.0.0" name: Financial Machine Learning description: "A curated list of practical financial machine learning tools and applications. financial machine learning, python, algorithmic-trading, cryptocurrency."
FinML Toolkit
A utility toolkit for logging, tracking, and managing financial ML operations. Each command records timestamped entries to its own log file for auditing and review.
发现更多技能插件，请访问7w4.net。
Commands
Core Operations
| Command | Description |
|---|---|
run <input> |
Log a run entry (view recent entries if no input given) |
check <input> |
Log a check entry for verification tasks |
convert <input> |
Log a convert entry for format conversion tasks |
analyze <input> |
Log an analyze entry for analysis tasks |
generate <input> |
Log a generate entry for generation tasks |
preview <input> |
Log a preview entry for preview tasks |
batch <input> |
Log a batch entry for batch processing tasks |
compare <input> |
Log a compare entry for comparison tasks |
export <input> |
Log an export entry for export tasks |
config <input> |
Log a config entry for configuration tasks |
status <input> |
Log a status entry for status tracking |
report <input> |
Log a report entry for reporting tasks |
Utility Commands
| Command | Description |
|---|---|
stats |
Show summary statistics across all log files |
export <fmt> |
Export all data in json, csv, or txt format |
search <term> |
Search all log entries for a term (case-insensitive) |
recent |
Show the 20 most recent entries from history |
status |
Health check — version, data dir, entry count, disk usage |
help |
Show available commands |
version |
Show version (v2.0.0) |
Data Storage
All data is stored in ~/.local/share/finml-toolkit/:
- Each command writes to its own log file (e.g.,
run.log,check.log,analyze.log) - All actions are also recorded in
history.logwith timestamps - Export files are written to the same directory as
export.json,export.csv, orexport.txt - Log format:
YYYY-MM-DD HH:MM|<input>(pipe-delimited)
Requirements
- Bash (no external dependencies)
- Works on Linux and macOS
When to Use
- When you need to log and track financial ML operations over time
- To maintain an audit trail of run, check, convert, analyze, or generate actions
- When you want to search or export historical operation records
- For batch tracking of ML processing pipelines
- To compare and report on financial data processing tasks
- When managing configurations for finml workflows
Examples
# Log operations
finml-toolkit run "backtest strategy alpha-3"
finml-toolkit check "validate portfolio weights"
finml-toolkit convert "csv to parquet format"
finml-toolkit analyze "correlation matrix on sector data"
finml-toolkit generate "monthly performance report"
finml-toolkit batch "process all Q4 earnings files"
finml-toolkit compare "strategy A vs strategy B returns"
finml-toolkit config "set risk_threshold=0.05"
# View recent entries for a command (no args)
finml-toolkit run
finml-toolkit analyze
# Search and export
finml-toolkit search "portfolio"
finml-toolkit export json
finml-toolkit stats
finml-toolkit recent
finml-toolkit status
Configuration
Set FINML_TOOLKIT_DIR environment variable to change the data directory. Default: ~/.local/share/finml-toolkit/
Output
All commands output to stdout. Redirect with finml-toolkit run > output.txt.
Powered by BytesAgain | bytesagain.com | hello@bytesagain.com
🤖 AI 评测
这个工具质量中等偏上，功能实用、文档清晰、使用简单是它的优点。它能帮你记录和追踪金融ML操作，方便以后查阅历史记录。纯 Bash 实现的优点是无需安装额外软件，缺点是功能相对基础，主要就是日志记录，缺少更高级的自动化或分析能力。对于日常记录需求来说够用，但如果你期望更复杂的金融分析功能可能会失望。
