S
SkillNav

Financial Machine Learning

by bytesagain1

Explore curated financial ML tools for trading, risk models, and predictions. Use when building models, benchmarking accuracy, or researching quant tools.

View Chinese version with editor review

安装

claude skill add --url github.com/openclaw/skills/tree/main/skills/bytesagain1/financial-machine-learning

文档

Financial Machine Learning

An AI toolkit for logging, tracking, and managing financial machine learning operations. Each command records timestamped entries to its own log file for auditing and review.

Commands

Core Operations

CommandDescription
configure <input>Log a configuration entry (view recent entries if no input given)
benchmark <input>Log a benchmark entry for performance testing
compare <input>Log a compare entry for model comparison tasks
prompt <input>Log a prompt entry for prompt engineering tasks
evaluate <input>Log an evaluate entry for model evaluation
fine-tune <input>Log a fine-tune entry for model fine-tuning tasks
analyze <input>Log an analyze entry for data analysis
cost <input>Log a cost entry for cost tracking
usage <input>Log a usage entry for usage monitoring
optimize <input>Log an optimize entry for optimization tasks
test <input>Log a test entry for testing tasks
report <input>Log a report entry for reporting

Utility Commands

CommandDescription
statsShow 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)
recentShow the 20 most recent entries from history
statusHealth check — version, data dir, entry count, disk usage
helpShow available commands
versionShow version (v2.0.0)

Data Storage

All data is stored in ~/.local/share/financial-machine-learning/:

  • Each command writes to its own log file (e.g., configure.log, benchmark.log, fine-tune.log)
  • All actions are also recorded in history.log with timestamps
  • Export files are written to the same directory as export.json, export.csv, or export.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 track financial ML experiments (benchmarks, fine-tuning, evaluations)
  • To maintain an audit trail of model configurations and prompt engineering
  • When comparing model performance across different runs
  • For tracking costs and usage across ML operations
  • To search or export historical experiment records
  • When building a log of optimization and testing iterations

Examples

bash
# Log ML operations
financial-machine-learning configure "set learning_rate=0.001"
financial-machine-learning benchmark "GPT-4 accuracy test on portfolio data"
financial-machine-learning fine-tune "BERT model on earnings calls"
financial-machine-learning evaluate "backtest Q3 predictions"
financial-machine-learning compare "LSTM vs transformer on forex data"
financial-machine-learning cost "API usage March 2025: $142"
financial-machine-learning optimize "hyperparameter sweep batch 3"
financial-machine-learning prompt "risk assessment template v2"

# View recent entries for a command (no args)
financial-machine-learning benchmark
financial-machine-learning cost

# Search and export
financial-machine-learning search "BERT"
financial-machine-learning export csv
financial-machine-learning stats
financial-machine-learning recent
financial-machine-learning status

Configuration

Set FINANCIAL_MACHINE_LEARNING_DIR environment variable to change the data directory. Default: ~/.local/share/financial-machine-learning/

Output

All commands output to stdout. Redirect with financial-machine-learning report > output.txt.

Powered by BytesAgain | bytesagain.com | hello@bytesagain.com