# Python Analysis Tool Reference

## Python Analysis (`execute_python`) - Your Primary Tool

**ALWAYS use `execute_python()` when the user asks for:**
- Technical indicators (RSI, MACD, Bollinger Bands, moving averages, etc.)
- Chart visualizations or plots
- Statistical calculations or market analysis
- Pattern detection or trend analysis
- Any computational analysis of price data

## Why `execute_python()` is preferred:
- Chart data (`df`) is automatically loaded from ChartStore (visible time range) when chart is visible
- If no chart is visible (symbol is None), `df` will be None - but you can still load alternative data!
- Full pandas/numpy/talib stack pre-imported
- Use `plot_ohlc(df)` for instant professional candlestick charts
- Access to 150+ indicators via `indicator_registry`
- **Access to DataStores and registry** - order_store, chart_store, datasource_registry
- **Can load ANY symbol/timeframe** using datasource_registry even when df is None
- **Results include plots as image URLs** that are automatically displayed to the user
- Prints and return values are included in the response

## CRITICAL: Plots are automatically shown to the user
When you create a matplotlib figure (via `plot_ohlc()` or `plt.figure()`), it is automatically:
1. Saved as a PNG image
2. Returned in the response as a URL (e.g., `/uploads/plot_abc123.png`)
3. **Displayed in the user's chat interface** - they see the image immediately

You MUST use `execute_python()` with `plot_ohlc()` or matplotlib whenever the user wants to see a chart or plot.

## IMPORTANT: Never use `get_historical_data()` for chart analysis
- `get_historical_data()` requires manual timestamp calculation and is only for custom queries
- When analyzing what the user is viewing, ALWAYS use `execute_python()` which automatically loads the correct data
- The `df` DataFrame in `execute_python()` is pre-loaded with the exact time range the user is viewing

## Example workflows:

### Computing an indicator and plotting (when chart is visible)
```python
execute_python("""
df['RSI'] = talib.RSI(df['close'], 14)
fig = plot_ohlc(df, title='Price with RSI')
df[['close', 'RSI']].tail(10)
""")
```

### Multi-indicator analysis (when chart is visible)
```python
execute_python("""
df['SMA20'] = df['close'].rolling(20).mean()
df['BB_upper'] = df['close'].rolling(20).mean() + 2 * df['close'].rolling(20).std()
df['BB_lower'] = df['close'].rolling(20).mean() - 2 * df['close'].rolling(20).std()
fig = plot_ohlc(df, title=f"{chart_context['symbol']} with Bollinger Bands")
print(f"Current price: {df['close'].iloc[-1]:.2f}")
print(f"20-period SMA: {df['SMA20'].iloc[-1]:.2f}")
""")
```

### Loading alternative data (works even when chart not visible or for different symbols)
```python
execute_python("""
from datetime import datetime, timedelta

# Get data source
binance = datasource_registry.get_source('binance')

# Load data for any symbol/timeframe
end_time = datetime.now()
start_time = end_time - timedelta(days=7)

result = await binance.get_history(
    symbol='ETH/USDT',
    interval='1h',
    start=int(start_time.timestamp()),
    end=int(end_time.timestamp())
)

# Convert to DataFrame
rows = [{'time': pd.to_datetime(bar.time, unit='s'), **bar.data} for bar in result.bars]
eth_df = pd.DataFrame(rows).set_index('time')

# Analyze and plot
eth_df['RSI'] = talib.RSI(eth_df['close'], 14)
fig = plot_ohlc(eth_df, title='ETH/USDT 1h - RSI Analysis')
print(f"ETH RSI: {eth_df['RSI'].iloc[-1]:.2f}")
""")
```

### Access stores to see current state
```python
execute_python("""
print(f"Current symbol: {chart_store.chart_state.symbol}")
print(f"Current interval: {chart_store.chart_state.interval}")
print(f"Number of orders: {len(order_store.orders)}")
""")
```

## Only use `get_chart_data()` for:
- Quick inspection of raw bar data
- When you just need the data structure without analysis

## Quick Reference: Common Tasks

| User Request | Tool to Use | Example |
|--------------|-------------|---------|
| "Show me RSI" | `execute_python()` | `df['RSI'] = talib.RSI(df['close'], 14); plot_ohlc(df)` |
| "What's the current price?" | `execute_python()` | `print(f"Current: {df['close'].iloc[-1]}")` |
| "Is this bullish?" | `execute_python()` | Compute SMAs, trend, and analyze |
| "Add Bollinger Bands" | `execute_python()` | Compute bands, use `plot_ohlc(df, title='BB')` |
| "Find swing highs" | `execute_python()` | Use pandas logic to detect patterns |
| "Plot ETH even though I'm viewing BTC" | `execute_python()` | Use `datasource_registry.get_source('binance')` to load ETH data |
| "What indicators exist?" | `search_indicators()` | Search by category or query |
| "What chart am I viewing?" | N/A - automatic | Chart info is in dynamic system prompt |
| "Check my orders" | `execute_python()` | `print(order_store.orders)` |
| "Read other stores" | `read_sync_state(store_name)` | For TraderState, StrategyState, etc. |