trading-visualization

Trading Visualization

Visualization is the primary interface between a trader and their data. Charts reveal patterns that tables and numbers cannot: breakdowns in strategy, regime transitions, clustering of losses, and the shape of risk. A well-designed chart communicates more in a glance than a page of statistics.

Three uses of trading charts:

1. Pattern recognition — Spot structural changes in price, volume, and momentum that quantitative filters miss.
2. Strategy evaluation — Equity curves, drawdown plots, and return distributions expose whether a strategy is robust or curve-fit.
3. Reporting — Communicate performance to stakeholders, journals, or your future self with publication-quality visuals.

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Chart Types Covered

Chart Type	Purpose	Library
Candlestick	OHLCV price action with overlays	mplfinance
Equity curve	Portfolio value over time	matplotlib
Drawdown	Underwater equity plot	matplotlib
Return distribution	Histogram + normal fit	matplotlib
Correlation heatmap	Cross-asset correlation matrix	matplotlib / seaborn
Trade markers	Entry/exit points on price chart	mplfinance / matplotlib
Indicator panels	RSI, MACD below price chart	mplfinance
Position timeline	When positions were held	matplotlib

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Libraries

mplfinance

Best for candlestick charts. Built on matplotlib with finance-specific defaults.

uv pip install mplfinance

import mplfinance as mpf

# Basic candlestick from a DataFrame with DatetimeIndex
# Columns: Open, High, Low, Close, Volume
mpf.plot(df, type="candle", volume=True, style="charles")

Key features:

• Native OHLCV support — pass a DataFrame directly
• Built-in volume bars
• addplot for overlays (moving averages, Bollinger Bands)
• Custom styles via mpf.make_mpf_style()

matplotlib

General purpose, most flexible. Use when you need full control over layout.

uv pip install matplotlib

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 1, figsize=(14, 8), height_ratios=[3, 1],
                         sharex=True)
axes[0].plot(dates, equity, color="#00ff88")
axes[1].fill_between(dates, drawdown, 0, color="#ff4444", alpha=0.5)

plotly

Interactive charts rendered as HTML. Best for exploration and dashboards.

uv pip install plotly

import plotly.graph_objects as go

fig = go.Figure(data=[go.Candlestick(
    x=df.index, open=df["Open"], high=df["High"],
    low=df["Low"], close=df["Close"]
)])
fig.update_layout(template="plotly_dark")
fig.write_html("chart.html")

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Styling: Dark Theme Default

Trading terminals use dark backgrounds by default. All charts in this skill follow that convention.

Quick dark theme setup

import matplotlib.pyplot as plt

plt.style.use("dark_background")
plt.rcParams.update({
    "figure.facecolor": "#1a1a2e",
    "axes.facecolor": "#1a1a2e",
    "axes.edgecolor": "#333333",
    "grid.color": "#333333",
    "grid.alpha": 0.4,
    "text.color": "#e0e0e0",
    "xtick.color": "#aaaaaa",
    "ytick.color": "#aaaaaa",
})

Trading color scheme

Element	Color	Hex
Bullish / profit	Green	#00ff88
Bearish / loss	Red	#ff4444
Neutral / info	Blue	#4488ff
Warning	Amber	#ffaa00
MA short	Orange	#ff6600
MA long	Blue	#3399ff
MA signal	Yellow	#ffcc00

See references/styling_guide.md for complete typography, layout ratios, and export settings.

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Chart Composition: Multi-Panel Layout

Most trading charts need multiple synchronized panels — price on top, volume in the middle, indicators at the bottom.

Stacked panels with shared x-axis

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

fig = plt.figure(figsize=(14, 10))
gs = gridspec.GridSpec(3, 1, height_ratios=[3, 1, 1], hspace=0.05)

ax_price = fig.add_subplot(gs[0])
ax_volume = fig.add_subplot(gs[1], sharex=ax_price)
ax_rsi = fig.add_subplot(gs[2], sharex=ax_price)

# Hide x-tick labels on upper panels
ax_price.tick_params(labelbottom=False)
ax_volume.tick_params(labelbottom=False)

Panel height ratios

Layout	Ratios	Use Case
Price + Volume	[3, 1]	Simple OHLCV chart
Price + Volume + Indicator	[3, 1, 1]	Standard analysis view
Equity + Drawdown	[2, 1]	Performance review
Price + RSI + MACD	[3, 1, 1]	Full indicator stack

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Candlestick Charts with Overlays

import mplfinance as mpf
import pandas as pd

# df: DataFrame with DatetimeIndex, columns Open/High/Low/Close/Volume
ema20 = df["Close"].ewm(span=20).mean()
ema50 = df["Close"].ewm(span=50).mean()

ap = [
    mpf.make_addplot(ema20, color="#ff6600", width=1.2),
    mpf.make_addplot(ema50, color="#3399ff", width=1.2),
]

style = mpf.make_mpf_style(
    base_mpf_style="nightclouds",
    marketcolors=mpf.make_marketcolors(
        up="#00ff88", down="#ff4444",
        wick={"up": "#00ff88", "down": "#ff4444"},
        edge={"up": "#00ff88", "down": "#ff4444"},
        volume={"up": "#00ff88", "down": "#ff4444"},
    ),
    facecolor="#1a1a2e", figcolor="#1a1a2e",
    gridcolor="#333333", gridstyle="--",
)

mpf.plot(df, type="candle", style=style, addplot=ap,
         volume=True, figsize=(14, 8),
         title="Token / SOL — 15m", savefig="candles.png")

---

Equity Curve with Drawdown Panel

import numpy as np
import matplotlib.pyplot as plt

def plot_equity_drawdown(equity: pd.Series, title: str = "Portfolio") -> plt.Figure:
    """Plot equity curve with drawdown panel below."""
    peak = equity.cummax()
    drawdown = (equity - peak) / peak

    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8),
                                    height_ratios=[2, 1], sharex=True)
    ax1.plot(equity.index, equity, color="#00ff88", linewidth=1.5)
    ax1.plot(equity.index, peak, color="#555555", linewidth=0.8,
             linestyle="--", label="Peak")
    ax1.set_title(title, fontsize=14, fontweight="bold", color="white")
    ax1.set_ylabel("Portfolio Value", fontsize=11)
    ax1.legend(loc="upper left")
    ax1.grid(True, alpha=0.3)

    ax2.fill_between(equity.index, drawdown, 0, color="#ff4444", alpha=0.5)
    ax2.set_ylabel("Drawdown", fontsize=11)
    ax2.set_xlabel("Date", fontsize=11)
    ax2.grid(True, alpha=0.3)

    fig.tight_layout()
    return fig

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Return Distribution

from scipy import stats

def plot_return_distribution(returns: pd.Series) -> plt.Figure:
    """Histogram of returns with normal fit and risk metrics."""
    fig, ax = plt.subplots(figsize=(10, 6))

    ax.hist(returns, bins=50, density=True, alpha=0.7,
            color="#4488ff", edgecolor="#333333")

    # Normal fit overlay
    mu, sigma = returns.mean(), returns.std()
    x = np.linspace(returns.min(), returns.max(), 200)
    ax.plot(x, stats.norm.pdf(x, mu, sigma), color="#ffaa00",
            linewidth=2, label=f"Normal(μ={mu:.4f}, σ={sigma:.4f})")

    # VaR line
    var_95 = returns.quantile(0.05)
    ax.axvline(var_95, color="#ff4444", linestyle="--",
               label=f"VaR 95%: {var_95:.4f}")

    ax.set_title("Return Distribution", fontsize=14, fontweight="bold")
    ax.set_xlabel("Return", fontsize=11)
    ax.legend()
    ax.grid(True, alpha=0.3)
    fig.tight_layout()
    return fig

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Correlation Heatmap

def plot_correlation_heatmap(returns_df: pd.DataFrame) -> plt.Figure:
    """Correlation matrix heatmap with annotations."""
    corr = returns_df.corr()
    fig, ax = plt.subplots(figsize=(10, 8))
    im = ax.imshow(corr, cmap="RdYlGn", vmin=-1, vmax=1, aspect="auto")

    ax.set_xticks(range(len(corr.columns)))
    ax.set_yticks(range(len(corr.columns)))
    ax.set_xticklabels(corr.columns, rotation=45, ha="right")
    ax.set_yticklabels(corr.columns)