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Trading Pattern Recon w Hurst - Forex.py

@@ -43,6 +43,7 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 import requests
+import time
 
 # -------------------------
 # Wavelets
@@ -150,7 +151,6 @@ DENOISE_WAVELET = "db4"              # DB family (Daubechies)
 DENOISE_LEVEL = 3
 DENOISE_MIN_LEN = 96
 DENOISE_THRESHOLD_MODE = "soft"
-
 DAYS_PER_YEAR = 252
 
 OUT_DIR = Path("./out_forex")
@@ -518,6 +518,125 @@ def plot_heatmap_monthly(r: pd.Series, title: str):
     return fig
 
 
+# ------------------------------------------------------------
+# Progress timer (post-test checkpoints)
+# ------------------------------------------------------------
+def _format_eta(seconds):
+    if seconds is None or seconds != seconds:
+        return "n/a"
+    seconds = max(0, int(round(seconds)))
+    minutes, secs = divmod(seconds, 60)
+    hours, minutes = divmod(minutes, 60)
+    if hours:
+        return f"{hours}h {minutes:02d}m {secs:02d}s"
+    return f"{minutes}m {secs:02d}s"
+
+_post_timer = {"t0": None, "tprev": None, "total": None, "done": 0}
+def start_post_timer(total_steps: int):
+    _post_timer["t0"] = time.perf_counter()
+    _post_timer["tprev"] = _post_timer["t0"]
+    _post_timer["total"] = total_steps
+    _post_timer["done"] = 0
+
+def checkpoint_post_timer(label: str):
+    if _post_timer["t0"] is None or _post_timer["total"] is None:
+        return
+    _post_timer["done"] += 1
+    now = time.perf_counter()
+    step_dt = now - _post_timer["tprev"]
+    total_dt = now - _post_timer["t0"]
+    avg = total_dt / max(_post_timer["done"], 1)
+    eta = avg * max(_post_timer["total"] - _post_timer["done"], 0)
+    print(f"[TIMER] post {_post_timer['done']}/{_post_timer['total']} {label} - step {step_dt:.2f}s, total {total_dt:.2f}s, ETA {_format_eta(eta)}")
+    _post_timer["tprev"] = now
+
+
+def _currency_allocation_from_exposure(exp_df: pd.DataFrame) -> pd.DataFrame:
+    """Convert net currency exposure to normalized gross allocation by currency."""
+    if exp_df is None or getattr(exp_df, "empty", True):
+        return pd.DataFrame()
+
+    W = exp_df.copy().apply(pd.to_numeric, errors="coerce").fillna(0.0)
+    if W.index.has_duplicates:
+        W = W[~W.index.duplicated(keep="last")]
+    W = W.sort_index()
+
+    W = W.abs()
+    keep_cols = [c for c in W.columns if float(np.abs(W[c]).sum()) > 0.0]
+    if keep_cols:
+        W = W[keep_cols]
+
+    row_sum = W.sum(axis=1).replace(0, np.nan)
+    W = W.div(row_sum, axis=0).fillna(0.0)
+    return W
+
+
+def plot_portfolio_composition_fixed(
+    weights: pd.DataFrame,
+    title: str,
+    save_path: Path | None = None,
+    max_legend: int = 20,
+):
+    """Stacked area dei pesi nel tempo (allocazione per valuta)."""
+    if weights is None or getattr(weights, "empty", True):
+        print(f"[SKIP] Nessun peso per: {title}")
+        return
+
+    W = weights.copy().apply(pd.to_numeric, errors="coerce").fillna(0.0)
+    if W.index.has_duplicates:
+        W = W[~W.index.duplicated(keep="last")]
+    W = W.sort_index()
+
+    keep_cols = [c for c in W.columns if float(np.abs(W[c]).sum()) > 0.0]
+    if not keep_cols:
+        print(f"[SKIP] Tutti i pesi sono zero per: {title}")
+        return
+    W = W[keep_cols]
+
+    if len(W.index) < 2:
+        print(f"[SKIP] Serie troppo corta per: {title}")
+        return
+
+    avg_w = W.mean(0).sort_values(ascending=False)
+    ordered = avg_w.index.tolist()
+
+    if len(ordered) > max_legend:
+        head = ordered[:max_legend]
+        tail = [c for c in ordered if c not in head]
+        W_show = W[head].copy()
+        if tail:
+            W_show["Other"] = W[tail].sum(1)
+            ordered = head + ["Other"]
+        else:
+            ordered = head
+    else:
+        W_show = W[ordered].copy()
+
+    cmap = plt.colormaps.get_cmap("tab20")
+    colors = [cmap(i % cmap.N) for i in range(len(ordered))]
+
+    fig, ax = plt.subplots(figsize=(11, 6))
+    ax.stackplot(W_show.index, [W_show[c].values for c in ordered], labels=ordered, colors=colors)
+    ax.set_title(f"Composizione valute nel tempo - {title}")
+    ymax = float(np.nanmax(W_show.sum(1).values))
+    if not np.isfinite(ymax) or ymax <= 0:
+        ymax = 1.0
+    ax.set_ylim(0, max(1.0, ymax))
+    ax.grid(True, alpha=0.3)
+    ax.set_ylabel("Peso")
+    ax.set_yticks(ax.get_yticks())
+    ax.set_yticklabels([f"{y*100:.0f}%" for y in ax.get_yticks()])
+
+    ncol = 2 if len(ordered) > 10 else 1
+    ax.legend(loc="upper left", bbox_to_anchor=(1.01, 1), frameon=False, ncol=ncol, title="Currency")
+    fig.tight_layout()
+
+    if save_path:
+        fig.savefig(save_path, dpi=150, bbox_inches="tight")
+        print(f"[INFO] Salvato: {save_path}")
+    plt.close(fig)
+
+
 def inverse_vol_weights(returns_df: pd.DataFrame, window: int, max_weight: float | None) -> pd.DataFrame:
     vol = returns_df.rolling(window).std()
     inv = 1 / vol.replace(0, np.nan)
@@ -686,6 +805,7 @@ def main():
     print(f"Fee: {FEE_BPS} bp | Short: {ALLOW_SHORT} | Currency cap: {CURRENCY_CAP:.2f}")
     print(f"Wavelet denoise: {DENOISE_ENABLED} ({DENOISE_WAVELET}, level={DENOISE_LEVEL}, min_len={DENOISE_MIN_LEN})")
     print("Esecuzione: close(t), PnL: close(t+1)/close(t)\n")
+    start_post_timer(5)
 
     # 1) Fetch prices
     prices: dict[str, pd.DataFrame] = {}
@@ -699,12 +819,16 @@ def main():
     if len(prices) < 5:
         raise RuntimeError(f"Pochi ticker validi ({len(prices)}).")
 
+    checkpoint_post_timer("Price fetch")
+
     # 2) Per ticker backtest
     hurst_rows = []
     summary_rows = []
     sig_rows = []
 
-    for tkr, dfp in prices.items():
+    total = len(prices)
+    for i, (tkr, dfp) in enumerate(prices.items(), 1):
+        print(f"[{i}/{total}] Testing {tkr} ...")
         if "AdjClose" not in dfp.columns:
             continue
 
@@ -757,6 +881,8 @@ def main():
     if not sig_rows:
         raise RuntimeError("Nessun ticker backtestato con successo")
 
+    checkpoint_post_timer("Per-ticker backtest")
+
     hurst_df = pd.DataFrame(hurst_rows).sort_values("Ticker").reset_index(drop=True)
     summary_df = pd.DataFrame(summary_rows).sort_values("Ticker").reset_index(drop=True)
     signals_df = pd.concat(sig_rows, ignore_index=True)
@@ -810,6 +936,8 @@ def main():
     eq_eq = equity_from_returns(ret_eq).rename("Eq_EqW_TopN")
     eq_rp = equity_from_returns(ret_rp).rename("Eq_RP_TopN")
 
+    checkpoint_post_timer("Portfolio build")
+
     # 6) Plots
     plt.figure(figsize=(10, 5))
     plt.plot(eq_eq, label=f"Equal Weight (Top{TOP_N}, fee {FEE_BPS}bp)")
@@ -829,6 +957,22 @@ def main():
     fig_rp.savefig(PLOT_DIR / "heatmap_rp.png", dpi=150)
     plt.close(fig_rp)
 
+    ccy_eq_alloc = _currency_allocation_from_exposure(ccy_eq)
+    ccy_rp_alloc = _currency_allocation_from_exposure(ccy_rp)
+
+    plot_portfolio_composition_fixed(
+        ccy_eq_alloc,
+        "Equal Weight (currency gross)",
+        PLOT_DIR / "composition_equal_weight_active.png",
+    )
+    plot_portfolio_composition_fixed(
+        ccy_rp_alloc,
+        "Risk Parity (currency gross)",
+        PLOT_DIR / "composition_risk_parity_active.png",
+    )
+
+    checkpoint_post_timer("Plots")
+
     # 7) Export
     hurst_df.to_csv(OUT_DIR / "hurst.csv", index=False)
     summary_df.to_csv(OUT_DIR / "forward_bt_summary.csv", index=False)
@@ -845,6 +989,7 @@ def main():
     ccy_eq.to_csv(OUT_DIR / "currency_exposure_eq.csv")
     ccy_rp.to_csv(OUT_DIR / "currency_exposure_rp.csv")
 
+    checkpoint_post_timer("Exports")
     print(f"\nSaved to: {OUT_DIR.resolve()}\n")
 
 

Trading Pattern Recon w Hurst - Stocks EU.py

@@ -23,6 +23,7 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 import requests
+import time
 
 # ------------------------------------------------------------
 # shared_utils import (local file next to this script)
@@ -520,6 +521,39 @@ def plot_heatmap_monthly(r: pd.Series, title: str):
     plt.tight_layout()
     return fig
 
+# ------------------------------------------------------------
+# Progress timer (post-test checkpoints)
+# ------------------------------------------------------------
+def _format_eta(seconds):
+    if seconds is None or seconds != seconds:
+        return 'n/a'
+    seconds = max(0, int(round(seconds)))
+    minutes, secs = divmod(seconds, 60)
+    hours, minutes = divmod(minutes, 60)
+    if hours:
+        return f"{hours}h {minutes:02d}m {secs:02d}s"
+    return f"{minutes}m {secs:02d}s"
+
+_post_timer = {'t0': None, 'tprev': None, 'total': None, 'done': 0}
+def start_post_timer(total_steps: int):
+    _post_timer['t0'] = time.perf_counter()
+    _post_timer['tprev'] = _post_timer['t0']
+    _post_timer['total'] = total_steps
+    _post_timer['done'] = 0
+
+def checkpoint_post_timer(label: str):
+    if _post_timer['t0'] is None or _post_timer['total'] is None:
+        return
+    _post_timer['done'] += 1
+    now = time.perf_counter()
+    step_dt = now - _post_timer['tprev']
+    total_dt = now - _post_timer['t0']
+    avg = total_dt / max(_post_timer['done'], 1)
+    eta = avg * max(_post_timer['total'] - _post_timer['done'], 0)
+    print(f"[TIMER] post {_post_timer['done']}/{_post_timer['total']} {label} - step {step_dt:.2f}s, total {total_dt:.2f}s, ETA {_format_eta(eta)}")
+    _post_timer['tprev'] = now
+
+
 
 def inverse_vol_weights(df: pd.DataFrame, window=60, max_weight=None) -> pd.DataFrame:
     """Inv-vol weights con cap hard (resta cash se i pesi cappati sommano < 1)."""
@@ -566,6 +600,76 @@ def make_active_weights(
     return res
 
 
+def plot_portfolio_composition_fixed(
+    weights: pd.DataFrame,
+    title: str,
+    save_path: Path | None = None,
+    max_legend: int = 20,
+):
+    """Stacked area dei pesi nel tempo (pesi attivi + Cash)."""
+    if weights is None or getattr(weights, "empty", True):
+        print(f"[SKIP] Nessun peso per: {title}")
+        return
+
+    W = weights.copy().apply(pd.to_numeric, errors="coerce").fillna(0.0)
+    if W.index.has_duplicates:
+        W = W[~W.index.duplicated(keep="last")]
+    W = W.sort_index()
+
+    keep_cols = [c for c in W.columns if float(np.abs(W[c]).sum()) > 0.0]
+    if not keep_cols:
+        print(f"[SKIP] Tutti i pesi sono zero per: {title}")
+        return
+    W = W[keep_cols]
+
+    if len(W.index) < 2:
+        print(f"[SKIP] Serie troppo corta per: {title}")
+        return
+
+    avg_w = W.mean(0).sort_values(ascending=False)
+    ordered = avg_w.index.tolist()
+    if "Cash" in ordered:
+        ordered = [c for c in ordered if c != "Cash"] + ["Cash"]
+
+    if len(ordered) > max_legend:
+        head = ordered[:max_legend]
+        if "Cash" not in head and "Cash" in ordered:
+            head = head[:-1] + ["Cash"]
+        tail = [c for c in ordered if c not in head]
+        W_show = W[head].copy()
+        if tail:
+            W_show["Other"] = W[tail].sum(1)
+            ordered = head + ["Other"]
+        else:
+            ordered = head
+    else:
+        W_show = W[ordered].copy()
+
+    cmap = plt.colormaps.get_cmap("tab20")
+    colors = [cmap(i % cmap.N) for i in range(len(ordered))]
+
+    fig, ax = plt.subplots(figsize=(11, 6))
+    ax.stackplot(W_show.index, [W_show[c].values for c in ordered], labels=ordered, colors=colors)
+    ax.set_title(f"Composizione portafoglio nel tempo - {title}")
+    ymax = float(np.nanmax(W_show.sum(1).values))
+    if not np.isfinite(ymax) or ymax <= 0:
+        ymax = 1.0
+    ax.set_ylim(0, max(1.0, ymax))
+    ax.grid(True, alpha=0.3)
+    ax.set_ylabel("Peso")
+    ax.set_yticks(ax.get_yticks())
+    ax.set_yticklabels([f"{y*100:.0f}%" for y in ax.get_yticks()])
+
+    ncol = 2 if len(ordered) > 10 else 1
+    ax.legend(loc="upper left", bbox_to_anchor=(1.01, 1), frameon=False, ncol=ncol, title="Ticker")
+    fig.tight_layout()
+
+    if save_path:
+        fig.savefig(save_path, dpi=150, bbox_inches="tight")
+        print(f"[INFO] Salvato: {save_path}")
+    plt.close(fig)
+
+
 def _build_dynamic_portfolio_returns(
     wide_pnl: pd.DataFrame,
     wide_sig: pd.DataFrame,
@@ -828,6 +932,7 @@ def calibrate_score_weights(
 # MAIN
 # ------------------------------------------------------------
 def main():
+    start_post_timer(5)
     # 1) Fetch prices
     prices = {}
     for tkr in TICKERS:
@@ -838,14 +943,19 @@ def main():
             print(f"[WARN] Skip {tkr}: {e}")
 
     if len(prices) < 5:
+
         raise RuntimeError(f"Pochi ticker validi ({len(prices)}). Controlla TICKERS e/o endpoint.")
 
+    checkpoint_post_timer("Price fetch")
+
     # 2) Backtest each ticker
     hurst_rows = []
     summary_rows = []
     signals_rows = []
 
-    for tkr, px in prices.items():
+    total = len(prices)
+    for i, (tkr, px) in enumerate(prices.items(), 1):
+        print(f"[{i}/{total}] Testing {tkr} ...")
         if not isinstance(px, pd.DataFrame) or "AdjClose" not in px.columns:
             print(f"[WARN] Serie senza AdjClose per {tkr}: skip")
             continue
@@ -898,6 +1008,8 @@ def main():
         })
         summary_rows.append(stats)
 
+    checkpoint_post_timer("Per-ticker backtest")
+
     if not signals_rows:
         raise RuntimeError("Nessun ticker backtestato con successo.")
 
@@ -969,6 +1081,8 @@ def main():
     eq_eq = equity_from_returns(ret_eq).rename("Eq_EqW_TopN")
     eq_rp = equity_from_returns(ret_rp).rename("Eq_RP_TopN")
 
+    checkpoint_post_timer("Portfolio build")
+
     # 5) Plots
     plt.figure(figsize=(10, 5))
     plt.plot(eq_eq, label=f"Equal Weight (Top{TOP_N})")
@@ -988,6 +1102,19 @@ def main():
     plt.savefig(PLOT_DIR / "heatmap_rp.png", dpi=150)
     plt.show()
 
+    plot_portfolio_composition_fixed(
+        dyn_port["w_eq_act"],
+        "Equal Weight (active + Cash)",
+        PLOT_DIR / "composition_equal_weight_active.png",
+    )
+    plot_portfolio_composition_fixed(
+        dyn_port["w_rp_act"],
+        "Risk Parity (active + Cash)",
+        PLOT_DIR / "composition_risk_parity_active.png",
+    )
+
+    checkpoint_post_timer("Plots")
+
     # 6) Save outputs
     hurst_df.to_csv(OUT_DIR / "hurst.csv", index=False)
     forward_bt_summary.to_csv(OUT_DIR / "forward_bt_summary.csv", index=False)
@@ -1000,6 +1127,7 @@ def main():
     # Portfolio metrics Excel (EW/RP) con fallback CSV
     save_portfolio_metrics(ret_eq, ret_rp, OUT_DIR / "portfolio_metrics.xlsx", TOP_N)
 
+    checkpoint_post_timer("Exports")
     print(f"\nSaved to: {OUT_DIR.resolve()}")
 
 

Trading Pattern Recon w Hurst - Stocks USA.py

@@ -23,6 +23,7 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 import requests
+import time
 
 # ------------------------------------------------------------
 # shared_utils import (local file next to this script)
@@ -515,6 +516,39 @@ def plot_heatmap_monthly(r: pd.Series, title: str):
     plt.tight_layout()
     return fig
 
+# ------------------------------------------------------------
+# Progress timer (post-test checkpoints)
+# ------------------------------------------------------------
+def _format_eta(seconds):
+    if seconds is None or seconds != seconds:
+        return 'n/a'
+    seconds = max(0, int(round(seconds)))
+    minutes, secs = divmod(seconds, 60)
+    hours, minutes = divmod(minutes, 60)
+    if hours:
+        return f"{hours}h {minutes:02d}m {secs:02d}s"
+    return f"{minutes}m {secs:02d}s"
+
+_post_timer = {'t0': None, 'tprev': None, 'total': None, 'done': 0}
+def start_post_timer(total_steps: int):
+    _post_timer['t0'] = time.perf_counter()
+    _post_timer['tprev'] = _post_timer['t0']
+    _post_timer['total'] = total_steps
+    _post_timer['done'] = 0
+
+def checkpoint_post_timer(label: str):
+    if _post_timer['t0'] is None or _post_timer['total'] is None:
+        return
+    _post_timer['done'] += 1
+    now = time.perf_counter()
+    step_dt = now - _post_timer['tprev']
+    total_dt = now - _post_timer['t0']
+    avg = total_dt / max(_post_timer['done'], 1)
+    eta = avg * max(_post_timer['total'] - _post_timer['done'], 0)
+    print(f"[TIMER] post {_post_timer['done']}/{_post_timer['total']} {label} - step {step_dt:.2f}s, total {total_dt:.2f}s, ETA {_format_eta(eta)}")
+    _post_timer['tprev'] = now
+
+
 
 def _portfolio_metric_row(name: str, r: pd.Series) -> dict:
     r = pd.to_numeric(r, errors="coerce").fillna(0.0)
@@ -606,6 +640,76 @@ def make_active_weights(
     return res
 
 
+def plot_portfolio_composition_fixed(
+    weights: pd.DataFrame,
+    title: str,
+    save_path: Path | None = None,
+    max_legend: int = 20,
+):
+    """Stacked area dei pesi nel tempo (pesi attivi + Cash)."""
+    if weights is None or getattr(weights, "empty", True):
+        print(f"[SKIP] Nessun peso per: {title}")
+        return
+
+    W = weights.copy().apply(pd.to_numeric, errors="coerce").fillna(0.0)
+    if W.index.has_duplicates:
+        W = W[~W.index.duplicated(keep="last")]
+    W = W.sort_index()
+
+    keep_cols = [c for c in W.columns if float(np.abs(W[c]).sum()) > 0.0]
+    if not keep_cols:
+        print(f"[SKIP] Tutti i pesi sono zero per: {title}")
+        return
+    W = W[keep_cols]
+
+    if len(W.index) < 2:
+        print(f"[SKIP] Serie troppo corta per: {title}")
+        return
+
+    avg_w = W.mean(0).sort_values(ascending=False)
+    ordered = avg_w.index.tolist()
+    if "Cash" in ordered:
+        ordered = [c for c in ordered if c != "Cash"] + ["Cash"]
+
+    if len(ordered) > max_legend:
+        head = ordered[:max_legend]
+        if "Cash" not in head and "Cash" in ordered:
+            head = head[:-1] + ["Cash"]
+        tail = [c for c in ordered if c not in head]
+        W_show = W[head].copy()
+        if tail:
+            W_show["Other"] = W[tail].sum(1)
+            ordered = head + ["Other"]
+        else:
+            ordered = head
+    else:
+        W_show = W[ordered].copy()
+
+    cmap = plt.colormaps.get_cmap("tab20")
+    colors = [cmap(i % cmap.N) for i in range(len(ordered))]
+
+    fig, ax = plt.subplots(figsize=(11, 6))
+    ax.stackplot(W_show.index, [W_show[c].values for c in ordered], labels=ordered, colors=colors)
+    ax.set_title(f"Composizione portafoglio nel tempo - {title}")
+    ymax = float(np.nanmax(W_show.sum(1).values))
+    if not np.isfinite(ymax) or ymax <= 0:
+        ymax = 1.0
+    ax.set_ylim(0, max(1.0, ymax))
+    ax.grid(True, alpha=0.3)
+    ax.set_ylabel("Peso")
+    ax.set_yticks(ax.get_yticks())
+    ax.set_yticklabels([f"{y*100:.0f}%" for y in ax.get_yticks()])
+
+    ncol = 2 if len(ordered) > 10 else 1
+    ax.legend(loc="upper left", bbox_to_anchor=(1.01, 1), frameon=False, ncol=ncol, title="Ticker")
+    fig.tight_layout()
+
+    if save_path:
+        fig.savefig(save_path, dpi=150, bbox_inches="tight")
+        print(f"[INFO] Salvato: {save_path}")
+    plt.close(fig)
+
+
 def _build_dynamic_portfolio_returns(
     wide_pnl: pd.DataFrame,
     wide_sig: pd.DataFrame,
@@ -823,6 +927,7 @@ def calibrate_score_weights(
 # MAIN
 # ------------------------------------------------------------
 def main():
+    start_post_timer(5)
     # 1) Fetch prices
     prices = {}
     for tkr in TICKERS:
@@ -833,14 +938,19 @@ def main():
             print(f"[WARN] Skip {tkr}: {e}")
 
     if len(prices) < 5:
+
         raise RuntimeError(f"Pochi ticker validi ({len(prices)}). Controlla TICKERS e/o endpoint.")
 
+    checkpoint_post_timer("Price fetch")
+
     # 2) Backtest each ticker
     hurst_rows = []
     summary_rows = []
     signals_rows = []
 
-    for tkr, px in prices.items():
+    total = len(prices)
+    for i, (tkr, px) in enumerate(prices.items(), 1):
+        print(f"[{i}/{total}] Testing {tkr} ...")
         if not isinstance(px, pd.DataFrame) or "AdjClose" not in px.columns:
             print(f"[WARN] Serie senza AdjClose per {tkr}: skip")
             continue
@@ -892,6 +1002,8 @@ def main():
         })
         summary_rows.append(stats)
 
+    checkpoint_post_timer("Per-ticker backtest")
+
     if not signals_rows:
         raise RuntimeError("Nessun ticker backtestato con successo.")
 
@@ -958,6 +1070,8 @@ def main():
     eq_eq = equity_from_returns(ret_eq).rename("Eq_EqW_TopN")
     eq_rp = equity_from_returns(ret_rp).rename("Eq_RP_TopN")
 
+    checkpoint_post_timer("Portfolio build")
+
     # 5) Plots
     plt.figure(figsize=(10, 5))
     plt.plot(eq_eq, label=f"Equal Weight (Top{TOP_N})")
@@ -977,6 +1091,19 @@ def main():
     plt.savefig(PLOT_DIR / "heatmap_rp.png", dpi=150)
     plt.show()
 
+    plot_portfolio_composition_fixed(
+        dyn_port["w_eq_act"],
+        "Equal Weight (active + Cash)",
+        PLOT_DIR / "composition_equal_weight_active.png",
+    )
+    plot_portfolio_composition_fixed(
+        dyn_port["w_rp_act"],
+        "Risk Parity (active + Cash)",
+        PLOT_DIR / "composition_risk_parity_active.png",
+    )
+
+    checkpoint_post_timer("Plots")
+
     # 6) Save outputs
     hurst_df.to_csv(OUT_DIR / "hurst.csv", index=False)
     forward_bt_summary.to_csv(OUT_DIR / "forward_bt_summary.csv", index=False)
@@ -987,6 +1114,7 @@ def main():
     pd.Series(base_tickers, name="TopN_Tickers").to_csv(OUT_DIR / "topn_tickers.csv", index=False)
     save_portfolio_metrics(ret_eq, ret_rp, OUT_DIR / "portfolio_metrics.xlsx", TOP_N)
 
+    checkpoint_post_timer("Exports")
     print(f"\nSaved to: {OUT_DIR.resolve()}")