""" ftmo_perspective_annual_v1.py ============================== Shows year-by-year performance from the FTMO challenge perspective. Key difference from annual_pnl_analysis_v1.py ----------------------------------------------- That script showed a COMPOUNDING account where balance grows from $100k to $1.2M but notional stays fixed. % returns shrink over time because the denominator grows. That is misleading for FTMO purposes. This script shows what ACTUALLY matters for prop firm challenges: - Fixed $100k account basis every year - Fixed $300k notional (0.75% of $100k always) - Dollar P&L vs $10k Phase 1 target - How many months each year would take to pass Phase 1 - How many months to pass Phase 2 (5% = $5k) The point: FTMO challenges always start at $100k with the same rules. The dollar P&L per year is what matters, not % of a growing balance. Also shows: - Monthly DD chart (not annual) so you can see the FTMO-relevant risk - Phase 1 and Phase 2 progress simulation per year Place this file in: C:\\Users\\paul_\\OneDrive\\fx_macro_intraday\\src\\research\\ftmo_perspective_annual_v1.py Run from project root: python src/research/ftmo_perspective_annual_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 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" # ── Constants ───────────────────────────────────────────────────────────────── CHALLENGE_BALANCE = 100_000.0 PHASE1_TARGET = 10_000.0 # 10% PHASE2_TARGET = 5_000.0 # 5% MAX_OVERALL_DD = 10_000.0 # 10% MAX_DAILY_DD = 5_000.0 # 5% STOP_PCT = 0.0025 BASE_RISK_PCT = 0.0075 BASE_NOTIONAL = (CHALLENGE_BALANCE * 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] # ── Load trades ─────────────────────────────────────────────────────────────── 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] if "zscore_abs" not in df.columns: df["zscore_abs"] = 2.75 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) df["year"] = df["exit_time"].dt.year df["month"] = df["exit_time"].dt.to_period("M") print(f" Loaded {len(df):,} trades from {fname}") return df.sort_values("exit_time").reset_index(drop=True) raise FileNotFoundError("No trade log found.") # ── Simulate challenge progress within a year ───────────────────────────────── def simulate_challenge_progress(grp: pd.DataFrame, target: float) -> dict: """ Simulates a challenge starting at $0 P&L, running through the year's trades in order. Returns when target is hit or year ends. """ grp = grp.sort_values("exit_time").reset_index(drop=True) cum = 0.0 peak = 0.0 max_dd = 0.0 daily_pnl = {} hit_target = False breach = False trades_to_target = None for i, row in grp.iterrows(): dk = str(row["exit_time"].date()) if dk not in daily_pnl: daily_pnl[dk] = 0.0 pnl = row["dollar_pnl"] cum += pnl daily_pnl[dk]+= pnl if cum > peak: peak = cum dd = peak - cum if dd > max_dd: max_dd = dd if daily_pnl[dk] < -MAX_DAILY_DD: breach = True break if cum <= -MAX_OVERALL_DD: breach = True break if cum >= target and not hit_target: hit_target = True trades_to_target = i + 1 break return { "final_pnl" : cum, "max_dd_dollar" : max_dd, "max_dd_pct" : max_dd / CHALLENGE_BALANCE * 100, "hit_target" : hit_target, "breach" : breach, "trades_to_target" : trades_to_target, "total_trades" : len(grp), } # ── Monthly P&L on fixed account ───────────────────────────────────────────── def monthly_pnl_fixed(df: pd.DataFrame) -> pd.DataFrame: monthly = (df.groupby("month")["dollar_pnl"] .sum() .reset_index()) monthly["month_dt"] = monthly["month"].dt.to_timestamp() monthly["pct"] = monthly["dollar_pnl"] / CHALLENGE_BALANCE * 100 return monthly # ── Print table ─────────────────────────────────────────────────────────────── def print_table(annual_rows: list): print(f"\n{'='*110}") print("FTMO CHALLENGE PERSPECTIVE — Fixed 100k Account | 300k Notional | Real Costs") print(f"{'='*110}") print(f"\n {'Year':<6}{'Trades':>7}{'PnL':>10}{'Pct':>7}" f"{'MaxDD%':>8}{'P1 Hit':>8}{'P1 Trades':>11}{'P1 Months':>11}" f"{'P2 Hit':>8}{'P2 Months':>11}{'Breach':>8}") print(f" {'─'*106}") hit_p1 = hit_p2 = breach_yr = 0 for r in annual_rows: p1h = "YES" if r["p1"]["hit_target"] else "no" p2h = "YES" if r["p2"]["hit_target"] else "no" br = "YES" if r["p1"]["breach"] or r["p2"]["breach"] else "-" pct = r["p1"]["final_pnl"] / CHALLENGE_BALANCE * 100 p1_trades = str(r["p1"]["trades_to_target"]) if r["p1"]["hit_target"] else "-" p2_trades = str(r["p2"]["trades_to_target"]) if r["p2"]["hit_target"] else "-" tpy = r["total_trades"] p1m = (r["p1"]["trades_to_target"] / tpy * 12 if r["p1"]["hit_target"] and tpy > 0 else float("nan")) p2m = (r["p2"]["trades_to_target"] / tpy * 12 if r["p2"]["hit_target"] and tpy > 0 else float("nan")) p1m_str = f"{p1m:.1f}m" if not np.isnan(p1m) else "-" p2m_str = f"{p2m:.1f}m" if not np.isnan(p2m) else "-" flag = "" if r["p1"]["hit_target"]: hit_p1 += 1 if r["p2"]["hit_target"]: hit_p2 += 1 if r["p1"]["breach"] or r["p2"]["breach"]: breach_yr += 1 print(f" {r['year']:<6}{r['total_trades']:>7}" f" {r['p1']['final_pnl']:>+8,.0f}" f"{pct:>7.1f}%" f"{r['p1']['max_dd_pct']:>8.2f}%" f"{p1h:>8}{p1_trades:>11}{p1m_str:>11}" f"{p2h:>8}{p2m_str:>11}" f"{br:>8}") print(f"\n {'─'*106}") n = len(annual_rows) avg_pnl = np.mean([r["p1"]["final_pnl"] for r in annual_rows]) avg_p1m = np.nanmean([ r["p1"]["trades_to_target"] / r["total_trades"] * 12 for r in annual_rows if r["p1"]["hit_target"] and r["total_trades"] > 0 ]) avg_p2m = np.nanmean([ r["p2"]["trades_to_target"] / r["total_trades"] * 12 for r in annual_rows if r["p2"]["hit_target"] and r["total_trades"] > 0 ]) avg_dd = np.mean([r["p1"]["max_dd_pct"] for r in annual_rows]) print(f"\n Phase 1 passed : {hit_p1}/{n} years ({hit_p1/n*100:.0f}%)") print(f" Phase 2 passed : {hit_p2}/{n} years ({hit_p2/n*100:.0f}%)") print(f" Breach years : {breach_yr}/{n}") print(f" Avg annual PnL : +{avg_pnl:>,.0f}") print(f" Avg Phase 1 time : {avg_p1m:.1f} months") print(f" Avg Phase 2 time : {avg_p2m:.1f} months") print(f" Avg combined total : {avg_p1m + avg_p2m:.1f} months") print(f" Avg year max DD : {avg_dd:.2f}% (limit 10%)") # ── Chart ───────────────────────────────────────────────────────────────────── def make_chart(df, annual_rows, monthly, save_path): BG = "#0d1117" PANEL = "#161b22" GRID = "#21262d" TEXT = "#e6edf3" MUTED = "#8b949e" CYAN = "#00d4ff" GREEN = "#00ff88" RED = "#ff4444" GOLD = "#ffd700" ORNG = "#ff8c00" fig = plt.figure(figsize=(22, 22), facecolor=BG) gs = gridspec.GridSpec(3, 2, figure=fig, hspace=0.50, wspace=0.28, left=0.07, right=0.97, top=0.92, bottom=0.05) def style(ax, title): ax.set_facecolor(PANEL) ax.set_title(title, color=TEXT, fontsize=11, fontweight="bold", pad=10) ax.tick_params(colors=TEXT, labelsize=9) for sp in ax.spines.values(): sp.set_color(GRID) ax.grid(True, color=GRID, linewidth=0.5, alpha=0.7) ax.yaxis.label.set_color(TEXT) ax.xaxis.label.set_color(TEXT) years = [r["year"] for r in annual_rows] pnl_vals = [r["p1"]["final_pnl"] for r in annual_rows] dd_vals = [r["p1"]["max_dd_pct"] for r in annual_rows] tpy_vals = [r["total_trades"] for r in annual_rows] def safe_months(r, phase): if r[phase]["hit_target"] and r["total_trades"] > 0: return r[phase]["trades_to_target"] / r["total_trades"] * 12 return np.nan p1_months = [safe_months(r, "p1") for r in annual_rows] p2_months = [safe_months(r, "p2") for r in annual_rows] comb_months = [ (p1 + p2) if not (np.isnan(p1) or np.isnan(p2)) else np.nan for p1, p2 in zip(p1_months, p2_months) ] # ── Panel 1: Annual PnL (dollar, fixed account) ──────────────────────── ax1 = fig.add_subplot(gs[0, 0]) style(ax1, "Annual PnL on Fixed 100k Account (300k notional)") cols = [GREEN if v >= 0 else RED for v in pnl_vals] bars = ax1.bar(years, [v / 1000 for v in pnl_vals], color=cols, alpha=0.85, width=0.7, zorder=3) ax1.axhline(y=PHASE1_TARGET / 1000, color=GOLD, linewidth=1.5, linestyle="--", alpha=0.9, label="P1 target (10k)") ax1.axhline(y=PHASE2_TARGET / 1000, color=CYAN, linewidth=1.2, linestyle=":", alpha=0.9, label="P2 target (5k)") ax1.axhline(y=0, color=MUTED, linewidth=0.8) for bar, val in zip(bars, pnl_vals): h = bar.get_height() ax1.text(bar.get_x() + bar.get_width() / 2, h + (0.3 if h >= 0 else -1.2), f"{val/1000:.0f}k", ha="center", va="bottom" if h >= 0 else "top", fontsize=7, color=TEXT, fontweight="bold") ax1.yaxis.set_major_formatter( mticker.FuncFormatter(lambda x, _: f"{x:.0f}k")) ax1.set_ylabel("PnL (thousands)", fontsize=9) ax1.set_xticks(years) ax1.set_xticklabels(years, rotation=45, fontsize=8) ax1.legend(facecolor=PANEL, edgecolor=GRID, labelcolor=TEXT, fontsize=8) # ── Panel 2: Challenge months per year ───────────────────────────────── ax2 = fig.add_subplot(gs[0, 1]) style(ax2, "Months to Pass Phase 1 + Phase 2 per Year") x = np.arange(len(years)) w = 0.35 valid_p1 = [v if not np.isnan(v) else 0 for v in p1_months] valid_p2 = [v if not np.isnan(v) else 0 for v in p2_months] b1 = ax2.bar(x - w/2, valid_p1, w, color=CYAN, alpha=0.8, label="Phase 1 months", zorder=3) b2 = ax2.bar(x + w/2, valid_p2, w, color=GREEN, alpha=0.8, label="Phase 2 months", zorder=3) # Combined line valid_comb = [v if not np.isnan(v) else np.nan for v in comb_months] ax2.plot(x, valid_comb, color=GOLD, linewidth=2, marker="D", markersize=5, zorder=5, label="Combined total") avg_comb = np.nanmean(valid_comb) ax2.axhline(y=avg_comb, color=GOLD, linewidth=1, linestyle="--", alpha=0.6, label=f"Avg combined: {avg_comb:.1f}m") ax2.set_xticks(x) ax2.set_xticklabels(years, rotation=45, fontsize=8) ax2.set_ylabel("Months", fontsize=9) ax2.legend(facecolor=PANEL, edgecolor=GRID, labelcolor=TEXT, fontsize=8, loc="upper right") for bar, val, yr_idx in zip(b1, valid_p1, range(len(years))): if val > 0: ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.05, f"{val:.1f}", ha="center", fontsize=6.5, color=CYAN) # ── Panel 3: Monthly PnL bars (full series, fixed account) ───────────── ax3 = fig.add_subplot(gs[1, :]) style(ax3, "Monthly PnL — Fixed 100k Account | Entire 22-Year History") mc = [GREEN if v >= 0 else RED for v in monthly["dollar_pnl"]] ax3.bar(range(len(monthly)), monthly["dollar_pnl"] / 1000, color=mc, alpha=0.8, width=1.0) ax3.axhline(y=0, color=MUTED, linewidth=0.8) ax3.axhline(y=PHASE1_TARGET / 1000 / 12, color=GOLD, linewidth=1, linestyle="--", alpha=0.6, label="Avg monthly needed for P1 (833/mo)") # Year separators and labels year_starts = [] prev_yr = None for i, m in enumerate(monthly["month_dt"]): yr = m.year if yr != prev_yr: year_starts.append((i, yr)) if i > 0: ax3.axvline(x=i, color=GRID, linewidth=1.2, alpha=0.8) prev_yr = yr ax3.set_xticks([i for i, _ in year_starts]) ax3.set_xticklabels([str(yr) for _, yr in year_starts], rotation=45, fontsize=8) ax3.yaxis.set_major_formatter( mticker.FuncFormatter(lambda x, _: f"{x:.0f}k")) ax3.set_ylabel("Monthly PnL (thousands)", fontsize=9) pos_months = (monthly["dollar_pnl"] > 0).sum() tot_months = len(monthly) ax3.text(0.01, 0.95, f"Positive months: {pos_months}/{tot_months} " f"({pos_months/tot_months*100:.0f}%)", transform=ax3.transAxes, ha="left", va="top", color=TEXT, fontsize=9, fontweight="bold") ax3.legend(facecolor=PANEL, edgecolor=GRID, labelcolor=TEXT, fontsize=8) # ── Panel 4: Annual max DD ───────────────────────────────────────────── ax4 = fig.add_subplot(gs[2, 0]) style(ax4, "Annual Max Drawdown (on 100k fixed account)") dd_cols = [GREEN if v < 2 else GOLD if v < 5 else RED for v in dd_vals] ax4.bar(years, [-v for v in dd_vals], color=dd_cols, alpha=0.85, width=0.7, zorder=3) ax4.axhline(y=-5, color=ORNG, linewidth=1.2, linestyle="--", alpha=0.8, label="Daily DD limit 5%") ax4.axhline(y=-10, color=RED, linewidth=1.2, linestyle="--", alpha=0.8, label="Overall DD limit 10%") ax4.yaxis.set_major_formatter( mticker.FuncFormatter(lambda x, _: f"{abs(x):.1f}%")) ax4.set_ylabel("Max Drawdown %", fontsize=9) ax4.set_xticks(years) ax4.set_xticklabels(years, rotation=45, fontsize=8) ax4.legend(facecolor=PANEL, edgecolor=GRID, labelcolor=TEXT, fontsize=8) for i, (yr, dd) in enumerate(zip(years, dd_vals)): ax4.text(yr, -dd - 0.15, f"{dd:.1f}%", ha="center", fontsize=7, color=TEXT) # ── Panel 5: Trade count and win rate ────────────────────────────────── ax5 = fig.add_subplot(gs[2, 1]) style(ax5, "Trades per Year and Win Rate") ax5b = ax5.twinx() ax5b.set_facecolor(PANEL) wr_vals = [] for r in annual_rows: yr_trades = df[df["year"] == r["year"]] wr_vals.append(yr_trades["win"].mean() * 100 if len(yr_trades) > 0 else 0) ax5.bar(years, tpy_vals, color=CYAN, alpha=0.3, width=0.7, zorder=2) ax5b.plot(years, wr_vals, color=GREEN, linewidth=2, marker="o", markersize=5, zorder=5) avg_wr = np.mean(wr_vals) ax5b.axhline(y=avg_wr, color=GREEN, linewidth=0.8, linestyle="--", alpha=0.5) ax5b.text(years[0], avg_wr + 1, f"avg {avg_wr:.1f}%", color=GREEN, fontsize=8) ax5.set_ylabel("Trades", fontsize=9, color=CYAN) ax5b.set_ylabel("Win Rate %", fontsize=9, color=GREEN) ax5.tick_params(axis="y", colors=CYAN) ax5b.tick_params(axis="y", colors=GREEN) for sp in ax5b.spines.values(): sp.set_color(GRID) ax5b.set_ylim(0, 105) ax5.set_xticks(years) ax5.set_xticklabels(years, rotation=45, fontsize=8) # ── Suptitle ─────────────────────────────────────────────────────────── avg_p1 = np.nanmean(p1_months) avg_p2 = np.nanmean(p2_months) avg_pnl = np.mean(pnl_vals) fig.suptitle( f"FTMO Challenge Perspective - Fixed 100k Account | 300k Notional\n" f"Avg Annual PnL: {avg_pnl/1000:.0f}k | " f"Avg Phase 1: {avg_p1:.1f} months | " f"Avg Phase 2: {avg_p2:.1f} months | " f"Avg Combined: {avg_p1+avg_p2:.1f} months | " f"Real-World Costs Applied", color=TEXT, fontsize=13, fontweight="bold", y=0.965 ) plt.savefig(save_path, dpi=150, bbox_inches="tight", facecolor=fig.get_facecolor()) plt.close() print(f" Chart saved: {save_path}") # ── Main ────────────────────────────────────────────────────────────────────── def main(): print("=" * 70) print("FTMO PERSPECTIVE ANNUAL ANALYSIS") print(f" Challenge account : 100k (fixed, not compounding)") print(f" Base notional : 300k (0.75% risk)") print(f" Phase 1 target : 10k (+10%)") print(f" Phase 2 target : 5k (+5%)") print(f" Daily DD limit : 5k (5%)") print(f" Overall DD limit : 10k (10%)") print("=" * 70) print("\nLoading trades...") df = load_trades() print("Running annual challenge simulations...") annual_rows = [] for year, grp in df.groupby("year"): p1 = simulate_challenge_progress(grp, PHASE1_TARGET) p2 = simulate_challenge_progress(grp, PHASE2_TARGET) annual_rows.append({ "year" : year, "total_trades": len(grp), "p1" : p1, "p2" : p2, }) print("Computing monthly P&L...") monthly = monthly_pnl_fixed(df) print_table(annual_rows) print("\nGenerating chart...") chart_path = CHARTS_DIR / "ftmo_perspective_chart.png" make_chart(df, annual_rows, monthly, chart_path) print(f"\n Chart saved to: {chart_path}") print(f"\n KEY INSIGHT:") print(f" The annual % return shrinks in later years because the") print(f" COMPOUNDING account balance grows ($100k -> $1.2M) but") print(f" the notional stays fixed at 300k.") print(f" For FTMO challenges this does NOT matter - every challenge") print(f" starts fresh at 100k with the same 300k notional.") print(f" The dollar PnL per year is what matters for prop firms.") if __name__ == "__main__": main()