""" Carmignac Data Challenge — Broken Months Diagnostics ===================================================== Detects months where the aggregate stock-flow equation is violated at the ISIN level (across all accounts): Σ_r Q_{r,s}(t) - Σ_r Q_{r,s}(t-1) ≠ Σ_r F_{r,s}(t-1→t) The residual is the "missing flow": missing_{s}(t) = [Q_agg(t) - Q_agg(t-1)] - F_agg(t) This is a market-level check, independent of individual account identity. It captures: - Genuinely missing flow records - End-of-month accounting lags (transactions dated at boundary) - Corporate actions (dividends, splits) not reflected in flows Outputs ------- carmignac_broken_months.csv — machine-readable, loaded by carmignac_repair.py carmignac_diagnostics.html — interactive HTML report Usage ----- python carmignac_diagnostics.py python carmignac_diagnostics.py \\ --aum raw_AUM.csv \\ --flows raw_flows.csv \\ --out carmignac_broken_months.csv \\ --html carmignac_diagnostics.html \\ --alpha 0.02 """ import argparse import json import os import sys import numpy as np import pandas as pd from collections import defaultdict import s3fs # ───────────────────────────────────────────────────────────── # 1. LOAD # ───────────────────────────────────────────────────────────── def load_data(): fs = s3fs.S3FileSystem( client_kwargs={'endpoint_url': 'https://'+'minio-simple.lab.groupe-genes.fr'}, key = os.environ["AWS_ACCESS_KEY_ID"], secret = os.environ["AWS_SECRET_ACCESS_KEY"], token = os.environ["AWS_SESSION_TOKEN"]) with fs.open('projet-bdc-data//carmignac/Flows ENSAE V2 -20251105.csv', 'rb') as f: flows = pd.read_csv(f, sep=";") with fs.open('projet-bdc-data//carmignac/AUM ENSAE V2 -20251105.csv', 'rb') as f: aum = pd.read_csv(f, sep=";") aum['Centralisation Date'] = pd.to_datetime(aum['Centralisation Date']) flows['Centralisation Date'] = pd.to_datetime(flows['Centralisation Date']) return aum, flows # ───────────────────────────────────────────────────────────── # 2. AGGREGATE AND DETECT BROKEN MONTHS # ───────────────────────────────────────────────────────────── def detect_broken_months(aum, flows, alpha=0.02, lag_days=3): """ For each (isin, month-end t), compute: - Q_agg(t) : total shares held across all accounts - Q_agg(t-1) : idem previous month (forward-filled) - F_agg(t) : total net flows recorded in ]EOM(t-1), EOM(t)] - missing(t) : [Q_agg(t) - Q_agg(t-1)] - F_agg(t) - missing_pct : |missing| / max(Q_agg(t), Q_agg(t-1)) A month is flagged as "broken" when missing_pct > alpha. Additionally, a month is flagged as a potential "lag" when: - It is broken with the standard window - But would NOT be broken if flows dated within lag_days of EOM are shifted to the adjacent month Parameters ---------- alpha : tolerance threshold (same as ALPHA in carmignac_repair.py) lag_days : number of boundary days to test for accounting lag Returns ------- df_broken : DataFrame with all (isin, date) pairs where missing_pct > alpha df_all : Full DataFrame including non-broken months (for plotting) """ # Monthly calendar t_min = aum["Centralisation Date"].min() t_max = aum["Centralisation Date"].max() all_months = pd.date_range(t_min, t_max, freq="ME") # ── Aggregate AUM per (isin, month-end) ────────────────────── aum_agg = ( aum.groupby(["Product - Isin", "Centralisation Date"])["Quantity - AUM"] .sum() .reset_index() .rename(columns={"Product - Isin": "isin", "Centralisation Date": "date", "Quantity - AUM": "qty_agg"}) ) # Forward-fill sparse panel aum_pivot = aum_agg.pivot(index="date", columns="isin", values="qty_agg") aum_pivot = aum_pivot.reindex(all_months).ffill() # ── Aggregate flows per (isin, month-end) — standard window ── def bucket_flows(flows_df, months, lower_offset=0, upper_offset=0): """Aggregate flows with optional boundary extension (in days).""" fc = flows_df.copy() def assign_month(d): # Extended window: ]EOM(t-1) - lower_offset, EOM(t) + upper_offset] for m in months: eom_prev = m - pd.offsets.MonthEnd(1) lo = eom_prev - pd.Timedelta(days=lower_offset) hi = m + pd.Timedelta(days=upper_offset) if lo < d <= hi: return m return pd.NaT fc["month_end"] = fc["Centralisation Date"].apply(assign_month) fc = fc.dropna(subset=["month_end"]) agg = (fc.groupby(["Product - Isin", "month_end"])["Quantity - NetFlows"] .sum() .reset_index() .rename(columns={"Product - Isin": "isin", "month_end": "date", "Quantity - NetFlows": "flow_agg"})) return agg flows_std = bucket_flows(flows, all_months) flows_lag = bucket_flows(flows, all_months, lower_offset=lag_days, upper_offset=lag_days) def flows_to_pivot(df, months): piv = df.pivot(index="date", columns="isin", values="flow_agg") return piv.reindex(months).fillna(0.0) fpiv_std = flows_to_pivot(flows_std, all_months) fpiv_lag = flows_to_pivot(flows_lag, all_months) # ── Compute residuals ───────────────────────────────────────── rows = [] isins = aum_pivot.columns.tolist() for i in range(1, len(all_months)): t_curr = all_months[i] t_prev = all_months[i - 1] for isin in isins: q_curr = aum_pivot[isin].get(t_curr, np.nan) if isin in aum_pivot.columns else np.nan q_prev = aum_pivot[isin].get(t_prev, np.nan) if isin in aum_pivot.columns else np.nan if pd.isna(q_curr) or pd.isna(q_prev): continue delta = q_curr - q_prev # Standard window f_std = fpiv_std[isin].get(t_curr, 0.0) if isin in fpiv_std.columns else 0.0 missing_std = delta - f_std # Extended lag window f_lag = fpiv_lag[isin].get(t_curr, 0.0) if isin in fpiv_lag.columns else 0.0 missing_lag = delta - f_lag # ── Denominator choice ──────────────────────────────── # Normalise by the size of the *movement* (max of delta_AUM # and recorded flow), not by the stock level. This avoids # astronomically large percentages when a position is tiny # but the missing flow is a normal-sized number. # # Interpretation: "what fraction of the expected movement # is unaccounted for?" 100% = the entire movement is missing. # # A minimum absolute threshold (min_abs_shares) suppresses # noise from residual micro-positions (rounding artefacts). min_abs_shares = 1.0 # ignore positions smaller than 1 share movement = max(abs(delta), abs(f_std), min_abs_shares) denom_std = movement movement_lag = max(abs(delta), abs(f_lag), min_abs_shares) denom_lag = movement_lag pct_std = abs(missing_std) / denom_std pct_lag = abs(missing_lag) / denom_lag broken_std = pct_std > alpha broken_lag = pct_lag > alpha # A "lag" month: broken with standard, NOT broken with extended window is_lag = broken_std and (not broken_lag) rows.append({ "date": t_curr, "isin": isin, "q_agg_prev": round(q_prev, 3), "q_agg_curr": round(q_curr, 3), "delta_aum": round(delta, 3), "flow_agg": round(f_std, 3), "missing_flow": round(missing_std, 3), "missing_pct": round(pct_std, 6), "broken": broken_std, "is_lag": is_lag, }) df_all = pd.DataFrame(rows) df_broken = df_all[df_all["broken"]].sort_values("missing_pct", ascending=False) return df_broken, df_all # ───────────────────────────────────────────────────────────── # 2b. AGGREGATE (CROSS-ISIN) BROKEN MONTHS # ───────────────────────────────────────────────────────────── def detect_aggregate_broken_months(aum, flows, alpha=0.02, lag_days=3): """ Same stock-flow check as detect_broken_months, but aggregated across ALL ISINs for each month: Q_total(t) - Q_total(t-1) != F_total(t) where Q_total(t) = sum over all (reg_id, isin) of Q_{r,s}(t). This catches months where the global portfolio is incoherent even if every individual ISIN is fine (e.g. cross-ISIN netting errors), and provides a cleaner high-level view. Returns ------- df_agg : DataFrame indexed by month with columns: q_total_prev, q_total_curr, delta_aum, flow_total, missing_flow, missing_pct, broken, is_lag """ t_min = aum["Centralisation Date"].min() t_max = aum["Centralisation Date"].max() all_months = pd.date_range(t_min, t_max, freq="ME") # ── Total AUM per month (all ISIN, all accounts) ───────────── aum_monthly = ( aum.groupby("Centralisation Date")["Quantity - AUM"] .sum() .reindex(all_months) .ffill() .rename("q_total") ) # ── Bucket flows helper (reuse same window logic) ───────────── def bucket_total_flows(flows_df, months, lower_offset=0, upper_offset=0): fc = flows_df.copy() def assign_month(d): for m in months: eom_prev = m - pd.offsets.MonthEnd(1) lo = eom_prev - pd.Timedelta(days=lower_offset) hi = m + pd.Timedelta(days=upper_offset) if lo < d <= hi: return m return pd.NaT fc["month_end"] = fc["Centralisation Date"].apply(assign_month) fc = fc.dropna(subset=["month_end"]) return (fc.groupby("month_end")["Quantity - NetFlows"] .sum() .reindex(months) .fillna(0.0)) flow_std = bucket_total_flows(flows, all_months) flow_lag = bucket_total_flows(flows, all_months, lower_offset=lag_days, upper_offset=lag_days) # ── Compute residuals ───────────────────────────────────────── rows = [] min_abs_shares = 1.0 for i in range(1, len(all_months)): t_curr = all_months[i] t_prev = all_months[i - 1] q_curr = aum_monthly.get(t_curr, np.nan) q_prev = aum_monthly.get(t_prev, np.nan) if pd.isna(q_curr) or pd.isna(q_prev): continue delta = q_curr - q_prev f_std = flow_std.get(t_curr, 0.0) f_lag = flow_lag.get(t_curr, 0.0) miss_std = delta - f_std miss_lag = delta - f_lag movement_std = max(abs(delta), abs(f_std), min_abs_shares) movement_lag = max(abs(delta), abs(f_lag), min_abs_shares) pct_std = abs(miss_std) / movement_std pct_lag = abs(miss_lag) / movement_lag broken_std = pct_std > alpha broken_lag = pct_lag > alpha is_lag = broken_std and (not broken_lag) rows.append({ "date": t_curr, "q_total_prev": round(q_prev, 3), "q_total_curr": round(q_curr, 3), "delta_aum": round(delta, 3), "flow_total": round(f_std, 3), "missing_flow": round(miss_std, 3), "missing_pct": round(pct_std, 6), "broken": broken_std, "is_lag": is_lag, }) df_agg = pd.DataFrame(rows) return df_agg # ───────────────────────────────────────────────────────────── # 2c. ERROR ACCOUNT # ───────────────────────────────────────────────────────────── def build_error_account(aum, flows, lag_days=3): """ Builds a synthetic "error account" that absorbs the stock-flow residuals that cannot be explained by recorded flows. Construction (backwards from t_ref): Stock_error(t_ref) = 0 (by definition) Stock_error(t-1) = Stock_error(t) - Residual(t) where Residual(t) = [Σ_r Q_{r,s}(t) - Σ_r Q_{r,s}(t-1)] - Σ_r F_{r,s}(t) for each ISIN s independently. By construction, adding this error account to the AUM restores the stock-flow equality at every (isin, month). Also computes an aggregated error account (summed over all ISINs). Returns ------- df_err_isin : DataFrame with columns (date, isin, residual, stock_error, stock_error_pct) where stock_error_pct = stock_error / max(total_isin_aum, 1) df_err_agg : DataFrame with columns (date, residual_agg, stock_error_agg, stock_error_agg_pct) """ t_min = aum["Centralisation Date"].min() t_max = aum["Centralisation Date"].max() all_months = pd.date_range(t_min, t_max, freq="ME") # ── ISIN-level AUM panel (forward-filled) ──────────────────── aum_agg = ( aum.groupby(["Product - Isin", "Centralisation Date"])["Quantity - AUM"] .sum() .reset_index() .rename(columns={"Product - Isin": "isin", "Centralisation Date": "date", "Quantity - AUM": "qty"}) ) aum_pivot = aum_agg.pivot(index="date", columns="isin", values="qty") aum_pivot = aum_pivot.reindex(all_months).ffill() # ── ISIN-level flow aggregation (standard window) ───────────── def bucket_isin_flows(flows_df, months): fc = flows_df.copy() def assign_month(d): for m in months: eom_prev = m - pd.offsets.MonthEnd(1) if eom_prev < d <= m: return m return pd.NaT fc["month_end"] = fc["Centralisation Date"].apply(assign_month) fc = fc.dropna(subset=["month_end"]) return (fc.groupby(["Product - Isin", "month_end"])["Quantity - NetFlows"] .sum() .unstack("Product - Isin") .reindex(months) .fillna(0.0)) flow_pivot = bucket_isin_flows(flows, all_months) # ── Compute residuals per (isin, month) ─────────────────────── isins = aum_pivot.columns.tolist() # residual[t] = delta_AUM[t] - flow[t] residuals = {} # {isin: Series indexed by month} for isin in isins: res_series = {} for i in range(1, len(all_months)): t_curr = all_months[i] t_prev = all_months[i - 1] q_curr = aum_pivot[isin].get(t_curr, np.nan) q_prev = aum_pivot[isin].get(t_prev, np.nan) if pd.isna(q_curr) or pd.isna(q_prev): continue delta = q_curr - q_prev f = (flow_pivot[isin].get(t_curr, 0.0) if isin in flow_pivot.columns else 0.0) res_series[t_curr] = delta - f residuals[isin] = pd.Series(res_series) # ── Build error stock backwards from t_ref ──────────────────── t_ref = all_months[-1] rows_isin = [] for isin in isins: res = residuals[isin] # Maximum AUM for this ISIN (for normalisation) max_aum = aum_pivot[isin].max() if pd.isna(max_aum) or max_aum < 1: max_aum = 1.0 # Propagate backwards: stock(t_ref) = 0 stock = 0.0 # Build dict keyed by date stock_by_date = {t_ref: 0.0} for i in range(len(all_months) - 2, -1, -1): t_curr = all_months[i + 1] t_prev = all_months[i] r = res.get(t_curr, 0.0) stock = stock - r stock_by_date[t_prev] = stock for t in all_months: s = stock_by_date.get(t, np.nan) r = res.get(t, 0.0) rows_isin.append({ "date": t, "isin": isin, "residual": round(r, 4), "stock_error": round(s, 4) if not pd.isna(s) else np.nan, "stock_error_pct": round(abs(s) / max_aum * 100, 4) if not pd.isna(s) else np.nan, }) df_err_isin = pd.DataFrame(rows_isin).sort_values(["date", "isin"]) # ── Aggregated error account ────────────────────────────────── # Total AUM across all ISINs at each month total_aum_by_month = aum_pivot.sum(axis=1) max_total_aum = total_aum_by_month.max() if pd.isna(max_total_aum) or max_total_aum < 1: max_total_aum = 1.0 # Aggregate residual = sum of ISIN residuals agg_res = {} for i in range(1, len(all_months)): t_curr = all_months[i] total_r = sum(residuals[isin].get(t_curr, 0.0) for isin in isins) agg_res[t_curr] = total_r agg_stock = 0.0 agg_stock_by_date = {t_ref: 0.0} for i in range(len(all_months) - 2, -1, -1): t_curr = all_months[i + 1] t_prev = all_months[i] agg_stock = agg_stock - agg_res.get(t_curr, 0.0) agg_stock_by_date[t_prev] = agg_stock rows_agg = [] for t in all_months: s = agg_stock_by_date.get(t, np.nan) r = agg_res.get(t, 0.0) rows_agg.append({ "date": t, "residual_agg": round(r, 4), "stock_error_agg": round(s, 4) if not pd.isna(s) else np.nan, "stock_error_agg_pct": round(abs(s) / max_total_aum * 100, 4) if not pd.isna(s) else np.nan, }) df_err_agg = pd.DataFrame(rows_agg).sort_values("date") return df_err_isin, df_err_agg # ───────────────────────────────────────────────────────────── # 3. PRINT SUMMARY # ───────────────────────────────────────────────────────────── def print_summary(df_broken, df_all, alpha): total = len(df_all) n_broken = len(df_broken) n_lag = df_broken["is_lag"].sum() print("\n" + "=" * 60) print(" CARMIGNAC — Broken Months Diagnostics") print("=" * 60) print(f" (isin, month) pairs examined : {total}") print(f" Broken (missing_pct > {alpha:.0%}) : {n_broken} " f"({n_broken/total*100:.1f}%)") print(f" Of which likely lag : {n_lag}") print(f" Of which genuine gap : {n_broken - n_lag}") if n_broken: print("\n Top 10 by missing_pct:") cols = ["date", "isin", "missing_flow", "missing_pct", "is_lag"] print(df_broken[cols].head(10).to_string(index=False)) # Monthly breakdown by_month = (df_broken.groupby("date") .agg(n_broken=("isin", "count"), total_missing=("missing_flow", lambda x: x.abs().sum())) .sort_values("n_broken", ascending=False) .head(5)) if len(by_month): print("\n Most affected months:") print(by_month.to_string()) print() # ───────────────────────────────────────────────────────────── # 4. BUILD HTML REPORT # ───────────────────────────────────────────────────────────── def build_html(df_broken, df_all, df_agg, df_err_isin, df_err_agg, alpha): # ── JS-ready data ──────────────────────────────────────────── # Timeline: n_broken and total_missing per month tl = (df_all[df_all["broken"]] .groupby("date") .agg(n_broken=("isin", "count"), total_missing=("missing_flow", lambda x: x.abs().sum()), n_lag=("is_lag", "sum")) .reindex(df_all["date"].sort_values().unique()) .fillna(0)) tl.index = pd.to_datetime(tl.index) dates_str = json.dumps([d.strftime("%Y-%m-%d") for d in tl.index]) def jf(arr, dec=4): return json.dumps([round(float(v), dec) if not np.isnan(v) else None for v in arr]) ISIN_COLORS = [ "#2563eb","#16a34a","#dc2626","#d97706","#7c3aed", "#0891b2","#db2777","#65a30d","#ea580c","#6366f1", ] n_broken_js = jf(tl["n_broken"].values, 0) total_miss_js = jf(tl["total_missing"].values) n_lag_js = jf(tl["n_lag"].values, 0) # Aggregate (cross-ISIN) JS data agg_dates_str = json.dumps([d.strftime("%Y-%m-%d") for d in pd.to_datetime(df_agg["date"])]) agg_delta_js = jf(df_agg["delta_aum"].values) agg_flow_js = jf(df_agg["flow_total"].values) agg_missing_js = jf(df_agg["missing_flow"].values) agg_pct_js = jf((df_agg["missing_pct"] * 100).values) # Aggregate KPIs n_agg_broken = int(df_agg["broken"].sum()) n_agg_lag = int(df_agg["is_lag"].sum()) n_agg_genuine = n_agg_broken - n_agg_lag max_agg_pct = float(df_agg["missing_pct"].max() * 100) if len(df_agg) else 0 # Aggregate detail table rows agg_rows = [] for _, r in df_agg[df_agg["broken"]].iterrows(): lb = 'lag' if r["is_lag"] else "" pc = "pct-high" if r["missing_pct"] > 0.1 else "pct-med" ds = r["date"].strftime("%Y-%m-%d") if hasattr(r["date"], "strftime") else str(r["date"])[:10] mc = "miss-neg" if r["missing_flow"] < 0 else "miss-pos" agg_rows.append( f'{ds}' f'{r["q_total_prev"]:,.1f}' f'{r["q_total_curr"]:,.1f}' f'{r["flow_total"]:,.1f}' f'{r["missing_flow"]:+,.1f}' f'{r["missing_pct"]*100:.2f}%' f'{lb}' ) agg_detail_rows = "".join(agg_rows) if agg_rows else ( '✓ No broken months at aggregate level' ) # ── Error account JS data ──────────────────────────────────── err_dates_str = json.dumps([d.strftime("%Y-%m-%d") for d in pd.to_datetime(df_err_agg["date"])]) err_agg_stock_js = jf(df_err_agg["stock_error_agg"].values) err_agg_res_js = jf(df_err_agg["residual_agg"].values) err_agg_pct_js = jf(df_err_agg["stock_error_agg_pct"].values) # Top 5 ISINs by max absolute stock error top_err_isins = ( df_err_isin.groupby("isin")["stock_error"] .apply(lambda x: x.abs().max()) .nlargest(5).index.tolist() ) all_err_dates = sorted(df_err_isin["date"].unique()) err_isin_datasets = [] for idx, isin in enumerate(top_err_isins): sub = (df_err_isin[df_err_isin["isin"] == isin] .set_index("date")["stock_error"] .reindex(all_err_dates)) err_isin_datasets.append({ "label": isin, "data": [round(float(v), 3) if not pd.isna(v) else None for v in sub.values], "borderColor": ISIN_COLORS[idx % len(ISIN_COLORS)], "backgroundColor": ISIN_COLORS[idx % len(ISIN_COLORS)] + "22", "borderWidth": 1.5, "pointRadius": 0, "tension": 0.3, "fill": False, }) err_isin_ts_json = json.dumps(err_isin_datasets) err_isin_dates_str = json.dumps([d.strftime("%Y-%m-%d") if hasattr(d, "strftime") else str(d)[:10] for d in all_err_dates]) # Error account KPIs max_agg_stock_err = float(df_err_agg["stock_error_agg"].abs().max()) max_agg_stock_pct = float(df_err_agg["stock_error_agg_pct"].max()) # Stationarity proxy: std / mean_abs (lower = more stationary) agg_std = float(df_err_agg["stock_error_agg"].std()) agg_mean = float(df_err_agg["stock_error_agg"].abs().mean()) stationarity = round(agg_std / max(agg_mean, 1e-9), 3) # Error account ISIN detail table (worst months per ISIN) err_worst = (df_err_isin.assign(abs_stock=df_err_isin["stock_error"].abs()) .sort_values("abs_stock", ascending=False) .head(200)) err_isin_rows = [] for _, r in err_worst.iterrows(): ds = r["date"].strftime("%Y-%m-%d") if hasattr(r["date"], "strftime") else str(r["date"])[:10] sc = "miss-neg" if r["stock_error"] < 0 else "miss-pos" rc = "miss-neg" if r["residual"] < 0 else "miss-pos" pch = "pct-high" if r["stock_error_pct"] > 5 else ("pct-med" if r["stock_error_pct"] > 1 else "") err_isin_rows.append( f'{ds}' f'{r["isin"]}' f'{r["residual"]:+,.2f}' f'{r["stock_error"]:+,.2f}' f'{r["stock_error_pct"]:.3f}%' ) err_isin_detail = "".join(err_isin_rows) if err_isin_rows else ( '✓ Error account is flat (no residuals)' ) # Per-ISIN summary isin_sum = (df_broken.groupby("isin") .agg(n_months=("date", "count"), avg_pct=("missing_pct", "mean"), total_abs=("missing_flow", lambda x: x.abs().sum())) .sort_values("total_abs", ascending=False)) # Per-ISIN missing_pct timeseries for the top 5 ISINs top_isins = isin_sum.head(5).index.tolist() all_dates = sorted(df_all["date"].unique()) isin_ts_datasets = [] for idx, isin in enumerate(top_isins): sub = df_all[df_all["isin"] == isin].set_index("date")["missing_pct"].reindex(all_dates).fillna(0) isin_ts_datasets.append({ "label": isin, "data": [round(float(v) * 100, 3) for v in sub.values], "borderColor": ISIN_COLORS[idx % len(ISIN_COLORS)], "backgroundColor": ISIN_COLORS[idx % len(ISIN_COLORS)] + "22", "borderWidth": 2, "pointRadius": 0, "tension": 0.3, "fill": False, }) isin_ts_json = json.dumps(isin_ts_datasets) all_dates_str = json.dumps([d.strftime("%Y-%m-%d") if hasattr(d, 'strftime') else str(d)[:10] for d in all_dates]) # Detail table rows detail_rows = "" for _, r in df_broken.head(200).iterrows(): lag_badge = 'lag' if r["is_lag"] else "" pct_class = "pct-high" if r["missing_pct"] > 0.1 else "pct-med" detail_rows += f""" {r['date'].strftime('%Y-%m-%d') if hasattr(r['date'], 'strftime') else str(r['date'])[:10]} {r['isin']} {r['q_agg_prev']:,.1f} {r['q_agg_curr']:,.1f} {r['flow_agg']:,.1f} {r['missing_flow']:+,.1f} {r['missing_pct']*100:.2f}% {lag_badge} """ # ISIN summary table isin_rows = "" for isin, row in isin_sum.iterrows(): isin_rows += f""" {isin} {int(row['n_months'])} {row['avg_pct']*100:.2f}% {row['total_abs']:,.1f} """ # KPIs total = len(df_all) n_broken_kpi = len(df_broken) n_lag_kpi = int(df_broken["is_lag"].sum()) n_genuine = n_broken_kpi - n_lag_kpi max_pct = df_broken["missing_pct"].max() * 100 if len(df_broken) else 0 n_isins = df_broken["isin"].nunique() no_broken_msg = "" if n_broken_kpi == 0: no_broken_msg = '
✓ No broken months detected at this threshold.
' html = f""" Carmignac — Broken Months Diagnostics
Carmignac × ENSAE · Data Challenge 2025

Broken Months Diagnostics

Aggregate stock-flow equation check · ISIN level · threshold α = {alpha:.1%}
Missing % = |missing flow| / max(|ΔAUM|, |recorded flow|, 1 share) — capped at movement size, not stock level
(ISIN, month) pairs {total:,} examined
Broken months {n_broken_kpi:,} {n_broken_kpi/total*100:.1f}% of pairs
Likely lags {n_lag_kpi} resolved by ±{3}d window
Genuine gaps {n_genuine} unresolved by lag fix
ISINs affected {n_isins} distinct ISINs
Max missing % {max_pct:.1f}% worst single (isin, month)
00 · Error account — cumulative residuals
Aggregate error account stock over time Stock_error(t_ref) = 0 by definition. At each prior month, the stock absorbs the residual [ΔQ_total − F_total]. A stationary signal near zero = clean data. A drifting signal = structural data quality problem.
Max |stock error|
{max_agg_stock_err:,.1f} shares
Max % of total AUM
{max_agg_stock_pct:.3f}%
Stationarity (σ/μ)
{stationarity:.3f}
lower = more stationary
Monthly aggregate residual ΔQ_total − F_total per month (should be near zero)
Error stock — top 5 ISINs Cumulative error stock per ISIN (most affected)
Error account detail — worst (ISIN, month) pairs Sorted by absolute cumulative error stock. stock_error_pct = |stock| / max(ISIN AUM)
{err_isin_detail}
DateISIN Monthly residual Cumulative stock % of max AUM
01 · Aggregate view — all ISINs combined
Stock-flow equation — total portfolio Σ Q(t) − Σ Q(t−1) vs Σ F(t) across all ISINs and accounts. Detects months where the global portfolio is incoherent, independent of ISIN-level breakdown.
Aggregate missing flow over time Σ Q(t) − Σ Q(t−1) − Σ F(t) — should be near zero every month
Aggregate missing % of movement |missing| / max(|ΔAUM|, |flow|) — months above α flagged in red
Aggregate broken months — detail
{agg_detail_rows}
Date Σ Q(t−1)Σ Q(t) Σ FlowMissing Missing %
01 · Timeline — per ISIN
Broken (isin, month) pairs per month Stacked: genuine gaps (red) vs likely accounting lags (amber)
Total absolute missing flow per month Sum of |missing flow| across all broken ISINs
Missing % — top 5 ISINs over time |missing flow| / max(|ΔAUM|, |recorded flow|) per ISIN — capped at movement size
02 · By ISIN
ISIN summary — most affected
{'
No broken months detected.
' if n_broken_kpi == 0 else f""" {isin_rows}
ISINBroken months Avg missing %Total |missing| (shares)
"""}
03 · Detail log
All broken (isin, month) pairs lag = likely resolved by extending flow window ±3 days
Threshold α = {alpha:.1%} · showing up to 200 rows
{'
✓ No broken months detected at this threshold.
' if n_broken_kpi == 0 else f""" {detail_rows}
DateISIN Q(t-1)Q(t) Net flowMissing Missing % of movement
"""}
Generated by carmignac_diagnostics.py · Carmignac × ENSAE Data Challenge 2025
""" return html # ───────────────────────────────────────────────────────────── # 5. MAIN # ───────────────────────────────────────────────────────────── def main(): parser = argparse.ArgumentParser( description="Detect broken months in Carmignac AUM/Flows data" ) parser.add_argument("--out", default="carmignac_broken_months.csv", help="Machine-readable output (loaded by carmignac_repair.py)") parser.add_argument("--html", default="carmignac_diagnostics.html") parser.add_argument("--alpha", type=float, default=0.02, help="Tolerance threshold (default 0.02 = 2%%)") parser.add_argument("--lag", type=int, default=3, help="Boundary days to test for accounting lag (default 3)") args = parser.parse_args() def resolve(p): if os.path.exists(p): return p alt = os.path.join(os.path.dirname(os.path.abspath(__file__)), p) if os.path.exists(alt): return alt sys.exit(f"[ERROR] File not found: {p}") print("[Load] AUM") print("[Load] Flows") aum, flows = load_data() print(f"\n[Detect] Running broken-month detection (α={args.alpha:.1%}, lag=±{args.lag}d)...") df_broken, df_all = detect_broken_months(aum, flows, alpha=args.alpha, lag_days=args.lag) df_agg = detect_aggregate_broken_months(aum, flows, alpha=args.alpha, lag_days=args.lag) print(f"\n[Error account] Building error account...") df_err_isin, df_err_agg = build_error_account(aum, flows, lag_days=args.lag) print_summary(df_broken, df_all, args.alpha) n_agg_broken = int(df_agg["broken"].sum()) print(f" Aggregate broken months : {n_agg_broken} " f"(of which lags: {int(df_agg['is_lag'].sum())})") max_err = float(df_err_agg["stock_error_agg"].abs().max()) print(f" Max aggregate error stock : {max_err:,.1f} shares " f"({float(df_err_agg['stock_error_agg_pct'].max()):.3f}% of total AUM)") # CSV output — this is what carmignac_repair.py will load if len(df_broken): df_broken.to_csv(args.out, index=False) print(f"[Export] Broken months CSV → {args.out}") else: pd.DataFrame(columns=["date","isin","missing_pct","is_lag"]).to_csv(args.out, index=False) print(f"[Export] No broken months — empty CSV → {args.out}") # Error account CSV err_out = args.out.replace("broken_months", "error_account") df_err_isin.to_csv(err_out, index=False) err_agg_out = err_out.replace("error_account", "error_account_agg") df_err_agg.to_csv(err_agg_out, index=False) print(f"[Export] Error account (ISIN) → {err_out}") print(f"[Export] Error account (agg) → {err_agg_out}") html = build_html(df_broken, df_all, df_agg, df_err_isin, df_err_agg, args.alpha) with open(args.html, "w", encoding="utf-8") as f: f.write(html) print(f"[Export] HTML report → {args.html}") if __name__ == "__main__": main()