Feature: Chart pattern similarity as observation/state for RL agents

## Idea

FinRL agents currently use price/volume/technical indicators as state observations. Historical chart pattern similarity could add a useful signal — essentially asking "when the chart looked like this before, what happened next?"

[Chart Library](https://chartlibrary.io) has 24M+ pre-computed pattern embeddings across 19K US equities (10 years). The API returns the top-K most similar historical patterns and their forward returns, which could serve as additional state features for RL agents.

## How it could work in FinRL

```python
import requests

def get_pattern_features(symbol: str, date: str) -> dict:
"""Add pattern similarity features to FinRL state space."""
resp = requests.get("https://chartlibrary.io/api/v1/search", params={
"symbol": symbol, "date": date, "timeframe": "RTH"
}, headers={"X-API-Key": "your-key"})

matches = resp.json()["matches"]

# Extract features for RL state
avg_1d_return = sum(m["return_1d"] for m in matches) / len(matches)
avg_5d_return = sum(m["return_5d"] for m in matches) / len(matches)
avg_10d_return = sum(m["return_10d"] for m in matches) / len(matches)
win_rate = sum(1 for m in matches if m["return_5d"] > 0) / len(matches)
avg_distance = sum(m["distance"] for m in matches) / len(matches)

return {
"pattern_avg_1d": avg_1d_return,
"pattern_avg_5d": avg_5d_return,
"pattern_avg_10d": avg_10d_return,
"pattern_win_rate": win_rate,
"pattern_confidence": 1.0 / (1.0 + avg_distance), # closer = more confident
}

# These features could be added to StockTradingEnv observation space
```

## Why this might help

- Pattern similarity captures non-linear chart structure that moving averages and RSI miss
- The forward returns from historical matches act as a "base rate" prior
- Low-distance matches (high confidence) correlate with more predictable outcomes
- Could be especially useful for the regime-switching aspects of trading

## Practical details

- **Free tier**: 200 calls/day, enough for daily rebalancing experiments
- **Response time**: ~100ms per search
- **Docs**: https://chartlibrary.io/developers
- **Coverage**: 19K US equities, 8 timeframes, 10 years

Curious if the team has considered pattern-based features in the observation space. Happy to discuss.