Z4 Finance

Getting Started

python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -r requirements.txt

Running the Application

# Example: latest stock data
python3 -m app.main AAPL

# Example: specific date
python3 -m app.main TSLA --date 2024-07-13

# Example: reddit demo
python3 -m app.reddit

Note: PRAW uses Reddit API credentials to access Reddit data. Replace the placeholder data with your actual credentials locally.

LLM Comment Analysis

Setup

1. Install and start Ollama (the script auto-pulls the model on first run)

Usage

# Defaults: gemma4:e4b (~8GB VRAM), test set, app/system_prompt.md
python3 -m app.run_llm

# Larger model (~16GB VRAM, e.g. RTX 5080) and a custom prompt
python3 -m app.run_llm --model gemma4:12b --system-prompt app/system_prompt.md \
  --input "raw data (placeholder)/train_fakecomments.json" \
  --output "processed data (placeholder)/llm_output_train.csv"

Output fields live in app/features.yaml (one entry = one validator + prompt line + CSV column). app/system_prompt.md holds the model's instructions/tone. The script appends to an existing output file if it exists, otherwise it creates a new one.