# Visualization

> Creates financial charts and visualizations. Supports candlestick charts, equity curves, indicator overlays, correlation heatmaps, and distribution plots. Trigger when the user wants to visualize financial data or trading results.

- Skill: `lisonevf/visualization` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lisonevf/visualization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lisonevf/visualization/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: lisonevf (https://skillmd.com/u/lisonevf)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/lisonevf/visualization

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# Visualization

Creates financial charts using matplotlib/mplfinance.

## Real Code Reference

- `tradinglearn/backtest/backtester.py` — `plot_results()` shows price vs portfolio overlay with buy/sell markers
- `tradinglearn/utils/parameter_optimizer.py` — `plot_optimization_results()` generates 4-panel heatmap figure

## Chart Types

- **Price charts**: candlestick (mplfinance), OHLC, line, area
- **Indicator overlays**: MACD subplot, RSI panel, Bollinger Bands on price
- **Performance**: equity curve, drawdown chart, rolling returns
- **Analysis**: correlation heatmap, return distribution histogram, scatter matrix
- **Comparison**: multi-stock overlay, benchmark vs strategy

## Usage

```python
from backtest.backtester import Backtester

bt = Backtester()
bt.run_backtest(data, strategy, params)
bt.plot_results()  # Price chart + portfolio value + buy/sell signals
```

For custom charts:
```python
import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 1, figsize=(12, 8))
axes[0].plot(portfolio['date'], portfolio['portfolio_value'])
axes[1].bar(trade_log['exit_date'], trade_log['pnl'])
```