Initial commit: multi-symbol bot with backtest engine and RSI trend strategy

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

reports/chart.py

@@ -0,0 +1,97 @@
+"""
+Equity curve and trade distribution charts.
+Saved to reports/ as PNG files.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use("Agg")   # non-interactive backend
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+from engine.backtest import Trade
+from engine.metrics  import Metrics
+
+REPORTS_DIR = Path(__file__).parent
+
+
+def save_equity_chart(
+    equity: np.ndarray,
+    trades: list[Trade],
+    metrics: Metrics,
+    label: str,
+    filename: str = "equity.png",
+) -> Path:
+    closed = [t for t in trades if t.closed]
+
+    # drawdown
+    peak = np.maximum.accumulate(equity)
+    dd   = (equity - peak) / peak * 100
+
+    # daily pnl histogram
+    by_day: dict[str, float] = {}
+    for t in closed:
+        day = t.exit_time.date().isoformat()          # type: ignore[union-attr]
+        by_day[day] = by_day.get(day, 0) + t.pnl_r
+    daily_r = np.array(list(by_day.values()))
+
+    fig = plt.figure(figsize=(14, 10))
+    fig.suptitle(label, fontsize=13, fontweight="bold")
+    gs  = gridspec.GridSpec(3, 2, figure=fig, hspace=0.45, wspace=0.3)
+
+    # ── equity curve ──────────────────────────────────────────────────────────
+    ax1 = fig.add_subplot(gs[0, :])
+    ax1.plot(equity, color="#2196F3", linewidth=1.2, label="Equity")
+    ax1.axhline(equity[0], color="grey", linewidth=0.6, linestyle="--")
+    ax1.set_ylabel("Balance ($)")
+    ax1.set_title("Equity Curve")
+    ax1.legend(fontsize=8)
+    ax1.grid(True, alpha=0.3)
+
+    # ── drawdown ──────────────────────────────────────────────────────────────
+    ax2 = fig.add_subplot(gs[1, :])
+    ax2.fill_between(range(len(dd)), dd, 0, color="#F44336", alpha=0.6)
+    ax2.set_ylabel("Drawdown (%)")
+    ax2.set_title(f"Drawdown  (max {metrics.max_drawdown*100:.1f}%)")
+    ax2.grid(True, alpha=0.3)
+
+    # ── R distribution ────────────────────────────────────────────────────────
+    ax3 = fig.add_subplot(gs[2, 0])
+    r_vals = [t.pnl_r for t in closed]
+    colors = ["#4CAF50" if r > 0 else "#F44336" for r in r_vals]
+    ax3.bar(range(len(r_vals)), r_vals, color=colors, width=0.8, alpha=0.8)
+    ax3.axhline(0, color="black", linewidth=0.6)
+    ax3.set_xlabel("Trade #")
+    ax3.set_ylabel("R")
+    ax3.set_title("R per Trade")
+    ax3.grid(True, alpha=0.3, axis="y")
+
+    # ── daily R histogram ─────────────────────────────────────────────────────
+    ax4 = fig.add_subplot(gs[2, 1])
+    if len(daily_r) > 0:
+        ax4.hist(daily_r, bins=30, color="#9C27B0", alpha=0.75, edgecolor="white")
+        ax4.axvline(0, color="black", linewidth=0.8)
+        mu, sigma = daily_r.mean(), daily_r.std()
+        ax4.set_title(f"Daily R  μ={mu:.3f}  σ={sigma:.3f}")
+    ax4.set_xlabel("Daily R")
+    ax4.set_ylabel("Days")
+    ax4.grid(True, alpha=0.3)
+
+    # stats annotation
+    stats = (
+        f"Trades: {metrics.n_trades}  WR: {metrics.win_rate*100:.1f}%\n"
+        f"PF: {metrics.profit_factor:.2f}  Sharpe: {metrics.sharpe:.2f}\n"
+        f"Return: {metrics.total_return*100:+.1f}%  MDD: {metrics.max_drawdown*100:.1f}%"
+    )
+    fig.text(0.5, 0.01, stats, ha="center", fontsize=9,
+             bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.4))
+
+    out = REPORTS_DIR / filename
+    plt.savefig(out, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    return out