Hermes Agent 51d6f7dfec docs(trading-scripts): add comprehensive README.md

- Quick start, examples, outputs
- AI integration guide
- Cron setup, troubleshooting

2026-04-26 23:34:30 +00:00

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Trading Scripts: Market Data Fetcher

Historical market data pipeline for AI analysis. Fetches stocks/futures/crypto, computes stats/charts, exports for uncensored LLMs.

🚀 Quick Start

# Install deps (one-time)
pip install yfinance pandas matplotlib seaborn plotly kaleido pyarrow

# Basic 1Y report
python market_data.py --period 1y --groups stocks futures crypto --output ./report-1y

# Live daily movers
python market_data.py --period 1d --groups meme --output ./today-movers

📊 Features

1Y+ History (auto-adjusts interval: 1d for long periods)
Futures-safe (flattens MultiIndex for ES=F etc., drops NaN gaps)
Stats: Total/annual returns, volatility, max drawdown, volume
Exports: CSV/Parquet/JSON + PNG charts + interactive HTML
Groups: stocks (AAPL/TSLA), futures (ES=F/NQ=F), crypto (BTC-USD), meme (GME)

Usage

python market_data.py [OPTIONS]

Options:
  --tickers AAPL,TSLA,ES=F     Comma-separated (default: AAPL,TSLA,ES=F,BTC-USD)
  --period 1y                  1mo|3mo|6mo|1y|2y|5y|10y|ytd|max (default: 1y)
  --interval 1d                1m|5m|1h|1d|1wk (auto-adjusts)
  --groups stocks futures      Add predefined groups
  --output ./reports           Output dir (default: ./market-historical)

Examples

Retail Biz Demo:

python market_data.py --period 1y --tickers AAPL,TSLA --output retail-stocks
# Feed report JSON to uncensored bot: "Analyze TSLA for landscaping firm cashflow"

Daily Alerts (Cron):

# Save daily to /opt/data/market-daily
0 9 * * 1-5 python /opt/gcloud-lab/trading-scripts/market_data.py --period 1d --output /opt/data/market-daily/today

Meme Stocks Live:

python market_data.py --period 5d --groups meme --interval 1h --output meme-watch

Outputs

report/
├── historical_data.csv          # Raw OHLCV
├── historical_data.parquet      # Efficient (AI load: pd.read_parquet)
├── historical_report.json       # Stats summary
├── historical_report.md         # Human-readable
├── historical_analysis.png      # Charts (normalized prices, returns, risk-return)
└── historical_interactive.html  # Zoomable Plotly

AI Integration (OpenClaw/Uncensored Bots)

import json
with open('report/historical_report.json') as f:
    data = json.load(f)

prompt = f"Analyze these 1Y stats for retail biz: {json.dumps(data['stats'])}"
# POST to vLLM: http://openclaw-brain-service:8000/v1/chat/completions

Troubleshooting

No data: Check market hours (futures/crypto 24/7)
MultiIndex error: Auto-handled (memory quirk fixed)
Large files: Use --period 1mo or Parquet
Deps: pyarrow for Parquet read/write

For clients: "Uncensored AI stock insights — privacy-first, no filters."

Built for gcloud-lab OpenClaw Brain. FluxCD deploys ready.

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