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Visualization utilities for plotting training results.
"""
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg") # Non-interactive backend for scripts
import matplotlib.pyplot as plt
from typing import List, Dict, Optional
import os
PLOT_DIR = "plots"
def _ensure_plot_dir(save_dir: str = PLOT_DIR):
os.makedirs(save_dir, exist_ok=True)
def plot_equity_curve(
episode_values: List[float],
title: str = "Equity Curve",
save_path: Optional[str] = None,
):
"""Plot portfolio value over time within an episode."""
_ensure_plot_dir()
fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(episode_values, color="#2196F3", linewidth=1.5)
ax.set_title(title, fontsize=14)
ax.set_xlabel("Step")
ax.set_ylabel("Portfolio Value ($)")
ax.grid(True, alpha=0.3)
ax.fill_between(range(len(episode_values)), episode_values,
alpha=0.1, color="#2196F3")
plt.tight_layout()
path = save_path or os.path.join(PLOT_DIR, "equity_curve.png")
fig.savefig(path, dpi=150)
plt.close(fig)
print(f"Saved: {path}")
return path
def plot_drawdown(
episode_values: List[float],
title: str = "Drawdown Chart",
save_path: Optional[str] = None,
):
"""Plot drawdown over time within an episode."""
_ensure_plot_dir()
values = np.array(episode_values)
peak = np.maximum.accumulate(values)
drawdown = (peak - values) / (peak + 1e-10)
fig, ax = plt.subplots(figsize=(12, 4))
ax.fill_between(range(len(drawdown)), drawdown, alpha=0.4, color="#F44336")
ax.plot(drawdown, color="#F44336", linewidth=1)
ax.set_title(title, fontsize=14)
ax.set_xlabel("Step")
ax.set_ylabel("Drawdown (%)")
ax.grid(True, alpha=0.3)
ax.invert_yaxis()
plt.tight_layout()
path = save_path or os.path.join(PLOT_DIR, "drawdown.png")
fig.savefig(path, dpi=150)
plt.close(fig)
print(f"Saved: {path}")
return path
def plot_reward_curve(
metrics: List[Dict],
baseline_metrics: Optional[List[Dict]] = None,
title: str = "Reward Curve Across Episodes",
save_path: Optional[str] = None,
):
"""Plot total reward per episode across training, optionally with baseline."""
_ensure_plot_dir()
rewards = [m["total_reward"] for m in metrics]
fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(rewards, color="#4CAF50", linewidth=1.5, label="Trained Agent", alpha=0.8)
# Smoothed trend
if len(rewards) > 5:
window = max(5, len(rewards) // 10)
smoothed = pd.Series(rewards).rolling(window=window, min_periods=1).mean()
ax.plot(smoothed, color="#2E7D32", linewidth=2.5, label="Trend (smoothed)")
# Baseline
if baseline_metrics:
bl_rewards = [m["total_reward"] for m in baseline_metrics]
bl_mean = float(np.mean(bl_rewards))
ax.axhline(y=bl_mean, color="#FF5722", linestyle="--", linewidth=2,
label=f"Random Baseline (avg={bl_mean:.3f})")
ax.set_title(title, fontsize=14)
ax.set_xlabel("Episode")
ax.set_ylabel("Total Reward")
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
path = save_path or os.path.join(PLOT_DIR, "reward_curve.png")
fig.savefig(path, dpi=150)
plt.close(fig)
print(f"Saved: {path}")
return path
def plot_grade_progression(
metrics: List[Dict],
baseline_metrics: Optional[List[Dict]] = None,
title: str = "Grade Progression (0 → 1)",
save_path: Optional[str] = None,
):
"""Plot grade progression across episodes."""
_ensure_plot_dir()
grades = [m["final_grade"] for m in metrics]
fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(grades, color="#9C27B0", linewidth=1.5, label="Trained Agent", alpha=0.8)
if len(grades) > 5:
window = max(5, len(grades) // 10)
smoothed = pd.Series(grades).rolling(window=window, min_periods=1).mean()
ax.plot(smoothed, color="#6A1B9A", linewidth=2.5, label="Trend (smoothed)")
if baseline_metrics:
bl_grades = [m["final_grade"] for m in baseline_metrics]
bl_mean = float(np.mean(bl_grades))
ax.axhline(y=bl_mean, color="#FF5722", linestyle="--", linewidth=2,
label=f"Random Baseline (avg={bl_mean:.3f})")
ax.set_title(title, fontsize=14)
ax.set_xlabel("Episode")
ax.set_ylabel("Grade [0, 1]")
ax.set_ylim(-0.05, 1.05)
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
path = save_path or os.path.join(PLOT_DIR, "grade_progression.png")
fig.savefig(path, dpi=150)
plt.close(fig)
print(f"Saved: {path}")
return path
def plot_comparison_table(
trained_metrics: List[Dict],
baseline_metrics: List[Dict],
save_path: Optional[str] = None,
):
"""Create a comparison table figure: random agent vs trained agent."""
_ensure_plot_dir()
def avg(metrics, key):
return np.mean([m[key] for m in metrics])
data = {
"Metric": ["Avg Reward", "Avg Grade", "Avg PnL %", "Avg Max DD", "Avg Sharpe"],
"Random Agent": [
f"{avg(baseline_metrics, 'total_reward'):.3f}",
f"{avg(baseline_metrics, 'final_grade'):.3f}",
f"{avg(baseline_metrics, 'pnl_pct'):.2%}",
f"{avg(baseline_metrics, 'max_drawdown'):.3f}",
f"{avg(baseline_metrics, 'sharpe_ratio'):.3f}",
],
"Trained Agent": [
f"{avg(trained_metrics, 'total_reward'):.3f}",
f"{avg(trained_metrics, 'final_grade'):.3f}",
f"{avg(trained_metrics, 'pnl_pct'):.2%}",
f"{avg(trained_metrics, 'max_drawdown'):.3f}",
f"{avg(trained_metrics, 'sharpe_ratio'):.3f}",
],
}
fig, ax = plt.subplots(figsize=(8, 3))
ax.axis("off")
table = ax.table(
cellText=list(zip(data["Metric"], data["Random Agent"], data["Trained Agent"])),
colLabels=["Metric", "Random Agent", "Trained Agent"],
cellLoc="center",
loc="center",
)
table.auto_set_font_size(False)
table.set_fontsize(11)
table.scale(1.2, 1.8)
# Style header
for j in range(3):
table[0, j].set_facecolor("#37474F")
table[0, j].set_text_props(color="white", fontweight="bold")
ax.set_title("Random vs Trained Agent Comparison", fontsize=14, pad=20)
plt.tight_layout()
path = save_path or os.path.join(PLOT_DIR, "comparison_table.png")
fig.savefig(path, dpi=150, bbox_inches="tight")
plt.close(fig)
print(f"Saved: {path}")
return path
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