""" account_growth_chart_v1.py =========================== Simple clean chart showing a single $100k account growing over 22 years. Every trade profit/loss is added back to the running balance. Notional stays fixed at $300k (0.75% of starting $100k) throughout — this is the conservative view showing the raw dollar engine of the model. Shows: - Single equity curve from $100k to final balance - Key milestones marked (250k, 500k, 750k, 1M) - Annual P&L bars below the curve - Key stats annotated on the chart Place this file in: C:\\Users\\paul_\\OneDrive\\fx_macro_intraday\\src\\research\\account_growth_chart_v1.py Run from project root: python src/research/account_growth_chart_v1.py """ import pandas as pd import numpy as np import matplotlib matplotlib.use("Agg") import matplotlib as mpl mpl.rcParams["text.usetex"] = False import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import matplotlib.ticker as mticker from matplotlib.patches import FancyArrowPatch from pathlib import Path import sys BASE_PATH = Path(__file__).resolve().parents[2] SRC_PATH = BASE_PATH / "src" if str(SRC_PATH) not in sys.path: sys.path.append(str(SRC_PATH)) TRADES_DIR = BASE_PATH / "data" / "processed" / "trades" CHARTS_DIR = BASE_PATH / "data" / "processed" ACCOUNT_START = 100_000.0 STOP_PCT = 0.0025 BASE_RISK_PCT = 0.0075 BASE_NOTIONAL = (ACCOUNT_START * BASE_RISK_PCT) / STOP_PCT # $300,000 ZSCORE_BANDS = [ (2.75, 3.50, 1.0), (3.50, 4.50, 1.5), (4.50, 99.0, 2.0), ] def get_multiplier(z): for lo, hi, mult in ZSCORE_BANDS: if lo <= z < hi: return mult return ZSCORE_BANDS[-1][2] def load_trades(): for fname in ["trades_real_costs.csv", "trades_eurusd_final.csv"]: path = TRADES_DIR / fname if path.exists(): df = pd.read_csv(path, parse_dates=["entry_time", "exit_time"]) ret_col = "return_real" if "return_real" in df.columns else "return" df["ret"] = df[ret_col] df["zscore_abs"] = df.get("zscore_abs", pd.Series([2.75]*len(df))) df["multiplier"] = df["zscore_abs"].apply(get_multiplier) df["notional"] = (BASE_NOTIONAL * df["multiplier"]).clip( upper=BASE_NOTIONAL * 3) df["dollar_pnl"] = df["ret"] * df["notional"] df["win"] = (df["ret"] > 0).astype(int) return df.sort_values("exit_time").reset_index(drop=True) raise FileNotFoundError("Run real_world_costs_v1.py first.") def main(): print("Loading trades...") df = load_trades() # Build running balance df["balance"] = ACCOUNT_START + df["dollar_pnl"].cumsum() df["peak"] = df["balance"].cummax() df["drawdown"] = (df["balance"] - df["peak"]) / df["peak"] * 100 df["year"] = df["exit_time"].dt.year # Annual P&L annual = df.groupby("year").agg( pnl = ("dollar_pnl", "sum"), trades = ("dollar_pnl", "count"), win_rate = ("win", "mean"), end_bal = ("balance", "last"), ).reset_index() final_bal = df["balance"].iloc[-1] total_ret = (final_bal / ACCOUNT_START - 1) * 100 cagr = (final_bal / ACCOUNT_START) ** (1/22) - 1 max_dd = df["drawdown"].min() avg_yr_pnl = annual["pnl"].mean() pos_years = (annual["pnl"] > 0).sum() avg_wr = df["win"].mean() * 100 print(f" Final balance : ${final_bal:>12,.0f}") print(f" Total return : {total_ret:.0f}%") print(f" CAGR : {cagr*100:.2f}%/yr") print(f" Max DD ever : {max_dd:.2f}%") # ── Chart ──────────────────────────────────────────────────────────────── BG = "#0d1117" PANEL = "#161b22" GRID = "#21262d" TEXT = "#e6edf3" MUTED = "#8b949e" GREEN = "#00ff88" CYAN = "#00d4ff" GOLD = "#ffd700" RED = "#ff4444" ORNG = "#ff8c00" fig = plt.figure(figsize=(20, 14), facecolor=BG) gs = gridspec.GridSpec(3, 1, figure=fig, height_ratios=[3, 1, 1], hspace=0.35, left=0.07, right=0.96, top=0.90, bottom=0.06) # ── Panel 1: Main equity curve ──────────────────────────────────────── ax1 = fig.add_subplot(gs[0]) ax1.set_facecolor(PANEL) ax1.tick_params(colors=TEXT, labelsize=9) for sp in ax1.spines.values(): sp.set_color(GRID) ax1.grid(True, color=GRID, linewidth=0.5, alpha=0.6) # Shaded area under curve ax1.fill_between(df["exit_time"], df["balance"] / 1e3, ACCOUNT_START / 1e3, alpha=0.12, color=GREEN) ax1.plot(df["exit_time"], df["balance"] / 1e3, color=GREEN, linewidth=1.8, zorder=5) # Starting line ax1.axhline(y=ACCOUNT_START / 1e3, color=MUTED, linewidth=0.8, linestyle="--", alpha=0.5) # Milestone lines milestones = [250_000, 500_000, 750_000, 1_000_000, 1_200_000] for m in milestones: if m <= final_bal * 1.05: ax1.axhline(y=m / 1e3, color=GRID, linewidth=0.6, linestyle=":", alpha=0.8) ax1.text(df["exit_time"].iloc[0], m / 1e3 + 5, f"{m/1e3:.0f}k", color=MUTED, fontsize=8, va="bottom") # Find when milestones were crossed and annotate milestone_labels = {250_000: "250k", 500_000: "500k", 750_000: "750k", 1_000_000: "1M"} for target, label in milestone_labels.items(): if final_bal >= target: idx = df[df["balance"] >= target].index[0] row = df.iloc[idx] ax1.scatter([row["exit_time"]], [row["balance"] / 1e3], color=GOLD, s=60, zorder=8, alpha=0.9) ax1.annotate( f"{label}\n{row['exit_time'].strftime('%b %Y')}", xy=(row["exit_time"], row["balance"] / 1e3), xytext=(12, 12), textcoords="offset points", color=GOLD, fontsize=7.5, fontweight="bold", arrowprops=dict(arrowstyle="-", color=GOLD, lw=0.8, alpha=0.6) ) # Final balance annotation ax1.scatter([df["exit_time"].iloc[-1]], [final_bal / 1e3], color=GREEN, s=80, zorder=9) ax1.annotate( f" Final: {final_bal/1e3:.0f}k", xy=(df["exit_time"].iloc[-1], final_bal / 1e3), xytext=(-100, 20), textcoords="offset points", color=GREEN, fontsize=12, fontweight="bold", arrowprops=dict(arrowstyle="->", color=GREEN, lw=1.5) ) ax1.yaxis.set_major_formatter( mticker.FuncFormatter(lambda x, _: f"{x:.0f}k")) ax1.set_ylabel("Account Balance", color=TEXT, fontsize=11) ax1.yaxis.label.set_color(TEXT) # Stats box top-left stats_text = ( f"Starting capital: 100,000\n" f"Final balance: {final_bal/1e3:.0f}k\n" f"Total return: {total_ret:.0f}%\n" f"CAGR: {cagr*100:.2f}% per year\n" f"Positive years: {pos_years}/22\n" f"Avg win rate: {avg_wr:.1f}%\n" f"Max drawdown: {max_dd:.2f}%\n" f"Avg annual PnL: {avg_yr_pnl/1e3:.0f}k" ) ax1.text(0.015, 0.97, stats_text, transform=ax1.transAxes, ha="left", va="top", color=TEXT, fontsize=9, fontfamily="monospace", bbox=dict(boxstyle="round,pad=0.5", facecolor="#1c2128", edgecolor=GRID, alpha=0.9)) ax1.set_title("Account Growth - 100k Starting Capital | 22 Years (2003-2026) |" " Fixed 300k Notional | Real-World Costs Applied", color=TEXT, fontsize=12, fontweight="bold", pad=12) # ── Panel 2: Annual P&L bars ────────────────────────────────────────── ax2 = fig.add_subplot(gs[1]) ax2.set_facecolor(PANEL) ax2.tick_params(colors=TEXT, labelsize=8) for sp in ax2.spines.values(): sp.set_color(GRID) ax2.grid(True, color=GRID, linewidth=0.4, alpha=0.6, axis="y") bar_cols = [GREEN if v >= 0 else RED for v in annual["pnl"]] bars = ax2.bar(annual["year"], annual["pnl"] / 1e3, color=bar_cols, alpha=0.85, width=0.7, zorder=3) ax2.axhline(y=0, color=MUTED, linewidth=0.8) ax2.axhline(y=10, color=GOLD, linewidth=1.0, linestyle="--", alpha=0.7, label="FTMO P1 target (10k)") for bar, val in zip(bars, annual["pnl"]): h = bar.get_height() ax2.text(bar.get_x() + bar.get_width()/2, h + (0.2 if h >= 0 else -1.0), f"{val/1e3:.0f}k", ha="center", va="bottom" if h >= 0 else "top", fontsize=6.5, color=TEXT, fontweight="bold") ax2.yaxis.set_major_formatter( mticker.FuncFormatter(lambda x, _: f"{x:.0f}k")) ax2.set_ylabel("Annual PnL", color=TEXT, fontsize=9) ax2.yaxis.label.set_color(TEXT) ax2.set_xticks(annual["year"]) ax2.set_xticklabels(annual["year"], rotation=45, fontsize=8) ax2.legend(facecolor=PANEL, edgecolor=GRID, labelcolor=TEXT, fontsize=8, loc="upper right") ax2.set_title("Annual PnL (gold line = FTMO Phase 1 target)", color=TEXT, fontsize=10, fontweight="bold", pad=8) # ── Panel 3: Drawdown ───────────────────────────────────────────────── ax3 = fig.add_subplot(gs[2]) ax3.set_facecolor(PANEL) ax3.tick_params(colors=TEXT, labelsize=8) for sp in ax3.spines.values(): sp.set_color(GRID) ax3.grid(True, color=GRID, linewidth=0.4, alpha=0.6) ax3.fill_between(df["exit_time"], df["drawdown"], 0, color=RED, alpha=0.35) ax3.plot(df["exit_time"], df["drawdown"], color=RED, linewidth=0.8, alpha=0.8) ax3.axhline(y=-5, color=ORNG, linewidth=1.0, linestyle="--", alpha=0.8, label="Daily DD limit 5%") ax3.axhline(y=-10, color=RED, linewidth=1.0, linestyle="--", alpha=0.8, label="Overall DD limit 10%") ax3.axhline(y=0, color=MUTED, linewidth=0.6, alpha=0.5) ax3.yaxis.set_major_formatter( mticker.FuncFormatter(lambda x, _: f"{abs(x):.1f}%")) ax3.set_ylabel("Drawdown", color=TEXT, fontsize=9) ax3.yaxis.label.set_color(TEXT) ax3.legend(facecolor=PANEL, edgecolor=GRID, labelcolor=TEXT, fontsize=8, loc="lower right") ax3.set_title("Running Drawdown (never came close to FTMO limits)", color=TEXT, fontsize=10, fontweight="bold", pad=8) ax3.text(0.01, 0.08, f"Worst ever drawdown: {max_dd:.2f}% vs FTMO limit: 10%", transform=ax3.transAxes, ha="left", va="bottom", color=RED, fontsize=9, fontweight="bold") # ── Main title ──────────────────────────────────────────────────────── fig.suptitle( f"EURUSD Macro Lead-Lag Model - Account Growth from 100k\n" f"22 Years | 4,019 Trades | 75% Win Rate | " f"CAGR {cagr*100:.1f}% | Final Balance: {final_bal/1e3:.0f}k | " f"Real-World Costs Applied", color=TEXT, fontsize=13, fontweight="bold", y=0.97 ) chart_path = CHARTS_DIR / "account_growth_chart.png" plt.savefig(chart_path, dpi=150, bbox_inches="tight", facecolor=fig.get_facecolor()) plt.close() print(f"\n Chart saved: {chart_path}") print(f"\n{'='*60}") print(f" 100k → {final_bal/1e3:.0f}k over 22 years") print(f" CAGR : {cagr*100:.2f}% per year") print(f" Every single year profit went straight back in") print(f" Max drawdown ever: {max_dd:.2f}%") print(f"{'='*60}") if __name__ == "__main__": main()