diff --git a/repair_challenge/carmignac repair.py b/repair_challenge/carmignac repair.py new file mode 100644 index 0000000..f8dd91c --- /dev/null +++ b/repair_challenge/carmignac repair.py @@ -0,0 +1,1037 @@ +""" +Carmignac Data Challenge — Pipeline Results Analysis +===================================================== +Analyses the CSV outputs produced by carmignac_repair.py: + - carmignac_scores.csv (post-surgery score history) + - carmignac_mapping.csv (reg_id mapping history) + - carmignac_surgery_log.csv (surgery operations) + +Produces a self-contained HTML report with interactive charts. + +Usage: + python carmignac_analysis.py + python carmignac_analysis.py --scores path/to/scores.csv \ + --mapping path/to/mapping.csv \ + --surgery path/to/surgery_log.csv \ + --out report.html +""" + +import argparse +import json +import os +import sys +import numpy as np +import pandas as pd + +# ───────────────────────────────────────────────────────────── +# 1. LOAD & VALIDATE +# ───────────────────────────────────────────────────────────── + +def load_outputs(scores_path, mapping_path, surgery_path): + scores = pd.read_csv(scores_path, parse_dates=["date"]) + mapping = pd.read_csv(mapping_path, parse_dates=["date"]) + surgery = pd.read_csv(surgery_path, parse_dates=["date"]) + + # Normalise dtypes + scores["reg_id"] = scores["reg_id"].astype(str) + mapping["reg_orig"] = mapping["reg_orig"].astype(str) + mapping["reg_used"] = mapping["reg_used"].astype(str) + mapping["changed"] = mapping["changed"].astype(bool) + surgery["reg_orig"] = surgery["reg_orig"].astype(str) + surgery["reg_from"] = surgery["reg_from"].astype(str) + surgery["reg_to"] = surgery["reg_to"].astype(str) + + return scores, mapping, surgery + + +# ───────────────────────────────────────────────────────────── +# 2. COMPUTE ANALYTICS +# ───────────────────────────────────────────────────────────── + +def compute_analytics(scores, mapping, surgery): + dates = sorted(scores["date"].unique()) + + # ── 2.1 Sum of scores per date (post-surgery) ────────────── + sum_post = (scores.groupby("date")["score"] + .sum() + .reindex(dates) + .rename("sum_post")) + + # ── 2.2 Reconstruct pre-surgery (counterfactual) ─────────── + # Without surgery, every reg_id that had a hard break would score 0 + # from that date backwards. We propagate the surgery "gain" as a + # cumulative deficit going back in time. + gain_by_date = surgery.groupby("date")["gain_vs_no_surgery"].sum() + # cumulative deficit = sum of gains for all surgeries at or after date t + cumulative_deficit = pd.Series(0.0, index=dates) + for d in dates: + cumulative_deficit[d] = gain_by_date[gain_by_date.index >= d].sum() + sum_pre = (sum_post - cumulative_deficit).clip(lower=0).rename("sum_pre") + + timeline = pd.DataFrame({"sum_post": sum_post, "sum_pre": sum_pre}) + timeline.index = pd.to_datetime(timeline.index) + timeline["recovery_pct"] = np.where( + sum_pre < sum_post, + (sum_post - sum_pre) / sum_post.clip(lower=1e-9) * 100, + 0.0, + ) + + # ── 2.3 Per-date surgery stats ───────────────────────────── + surgery_stats = ( + surgery.groupby("date") + .agg( + n_surgeries = ("reg_orig", "count"), + total_gain = ("gain_vs_no_surgery", "sum"), + avg_gain = ("gain_vs_no_surgery", "mean"), + avg_jaccard = ("jaccard_composite", "mean"), + avg_score_before = ("score_before", "mean"), + avg_score_after = ("score_after", "mean"), + ) + .reindex(dates, fill_value=0) + ) + + # ── 2.4 Score distribution over time ─────────────────────── + # Wide format: rows=dates, cols=reg_ids + pivot = scores.pivot_table(index="date", columns="reg_id", + values="score", aggfunc="last") + pivot = pivot.reindex(dates) + pivot.index = pd.to_datetime(pivot.index) + + # ── 2.5 Mapping churn ────────────────────────────────────── + # For each date, how many reg_ids are remapped (not using their original code)? + churn = (mapping.groupby("date")["changed"] + .sum() + .reindex(dates, fill_value=0) + .rename("n_remapped")) + + # ── 2.6 Score entropy (distribution spread) ──────────────── + def entropy(row): + p = row.dropna() + p = p[p > 0] + if len(p) == 0: + return np.nan + p = p / p.sum() + return -(p * np.log(p)).sum() + + timeline["entropy"] = pivot.apply(entropy, axis=1).values + + # ── 2.7 Individual score trajectories ────────────────────── + # Identify which reg_ids were ever remapped + ever_remapped = set(mapping.loc[mapping["changed"], "reg_orig"].unique()) + + # ── 2.8 Surgery detail table ─────────────────────────────── + surgery_detail = surgery.copy() + surgery_detail["gain_pct_of_score"] = ( + surgery_detail["gain_vs_no_surgery"] + / surgery_detail["score_before"].clip(lower=1e-9) * 100 + ).round(2) + + return { + "timeline": timeline, + "surgery_stats": surgery_stats, + "pivot": pivot, + "churn": churn, + "ever_remapped": ever_remapped, + "surgery_detail": surgery_detail, + "dates": [d.strftime("%Y-%m-%d") for d in dates], + } + + +# ───────────────────────────────────────────────────────────── +# 3. PRINT CONSOLE SUMMARY +# ───────────────────────────────────────────────────────────── + +def print_summary(analytics, surgery): + tl = analytics["timeline"] + ss = analytics["surgery_stats"] + + print("\n" + "=" * 65) + print(" CARMIGNAC PIPELINE — RESULTS SUMMARY") + print("=" * 65) + + print(f"\n Date range : {tl.index.min().date()} → {tl.index.max().date()}") + print(f" Total months : {len(tl)}") + print(f" Reg IDs : {analytics['pivot'].shape[1]}") + + print(f"\n ── Score (Σ) ──────────────────────────────────────────") + print(f" At t_ref (latest) : {tl['sum_post'].iloc[-1]:.6f}") + print(f" At t_min (earliest): {tl['sum_post'].iloc[0]:.6f}") + print(f" Min (post-surgery) : {tl['sum_post'].min():.6f} " + f"({tl['sum_post'].idxmin().date()})") + print(f" Min (pre-surgery) : {tl['sum_pre'].min():.6f} " + f"({tl['sum_pre'].idxmin().date()})") + print(f" Max recovery (pct) : {tl['recovery_pct'].max():.2f}%") + + print(f"\n ── Surgeries ─────────────────────────────────────────") + if len(surgery) == 0: + print(" No surgeries performed.") + else: + print(f" Total operations : {len(surgery)}") + print(f" Total score gained : {surgery['gain_vs_no_surgery'].sum():.6f}") + print(f" Avg Jaccard : {surgery['jaccard_composite'].mean():.4f}") + print(f" Avg gain / surgery : {surgery['gain_vs_no_surgery'].mean():.6f}") + print() + print(f" {'Date':12s} {'Reg orig':12s} {'From':15s} {'To':15s} " + f"{'Jaccard':>8s} {'Gain':>10s}") + print(" " + "-" * 78) + for _, row in surgery.sort_values("date").iterrows(): + print(f" {str(row['date'].date()):12s} {row['reg_orig']:12s} " + f"{row['reg_from']:15s} {row['reg_to']:15s} " + f"{row['jaccard_composite']:8.4f} {row['gain_vs_no_surgery']:10.6f}") + + print(f"\n ── Mapping churn ─────────────────────────────────────") + ch = analytics["churn"] + print(f" Max remapped at one date : {int(ch.max())} ({ch.idxmax().date() if ch.max()>0 else 'N/A'})") + print(f" Reg IDs ever remapped : {len(analytics['ever_remapped'])}") + + print(f"\n ── Score entropy (distribution spread) ───────────────") + ent = analytics["timeline"]["entropy"] + print(f" Mean entropy : {ent.mean():.4f}") + print(f" Std entropy : {ent.std():.4f}") + print() + + +# ───────────────────────────────────────────────────────────── +# 4. BUILD HTML REPORT +# ───────────────────────────────────────────────────────────── + +def build_html(analytics, surgery, scores, mapping): + tl = analytics["timeline"] + ss = analytics["surgery_stats"] + piv = analytics["pivot"] + ch = analytics["churn"] + dates_str = analytics["dates"] + + # ── helpers to serialise for JS ───────────────────────────── + def jf(arr, decimals=6): + return json.dumps([round(float(v), decimals) if not np.isnan(v) else None + for v in arr]) + + def js(arr): + return json.dumps(list(arr)) + + # ── colour palette ─────────────────────────────────────────── + REG_COLORS = [ + "#2563eb","#16a34a","#dc2626","#d97706","#7c3aed", + "#0891b2","#db2777","#65a30d","#ea580c","#6366f1", + "#059669","#b45309","#9333ea","#0284c7","#e11d48", + ] + + # ── 4.1 Surgery sparkline data ────────────────────────────── + surg_dates = [d.strftime("%Y-%m-%d") for d in ss.index] + n_surg = jf(ss["n_surgeries"].values, 0) + total_gain = jf(ss["total_gain"].values) + avg_gain = jf(ss["avg_gain"].values) + avg_jaccard = jf(ss["avg_jaccard"].values) + + # ── 4.2 Individual trajectories ──────────────────────────── + reg_ids = list(piv.columns) + traj_datasets = [] + for idx, rid in enumerate(reg_ids): + col = analytics["ever_remapped"] + dashed = rid in col + traj_datasets.append({ + "label": rid, + "data": [round(float(v), 6) if not np.isnan(v) else None + for v in piv[rid].values], + "borderColor": REG_COLORS[idx % len(REG_COLORS)], + "backgroundColor": REG_COLORS[idx % len(REG_COLORS)] + "22", + "borderWidth": 2 if not dashed else 2, + "borderDash": [] if not dashed else [6, 3], + "pointRadius": 0, + "tension": 0.3, + "fill": False, + }) + + traj_json = json.dumps(traj_datasets) + + # ── 4.3 Surgery detail table rows ────────────────────────── + sd = analytics["surgery_detail"].sort_values("date") + surg_rows_html = "" + if len(sd) == 0: + surg_rows_html = "No surgeries performed" + else: + for _, r in sd.iterrows(): + gain_class = "gain-high" if r["gain_vs_no_surgery"] > 0.05 else "gain-low" + surg_rows_html += f""" + + {r['date'].date()} + {r['reg_orig']} + {r['reg_from']} + → + {r['reg_to']} + {r['jaccard_composite']:.4f} + +{r['gain_vs_no_surgery']:.6f} + {r['gain_pct_of_score']:.1f}% + """ + + # ── 4.4 Top accounts table ────────────────────────────────── + last_date = piv.index.max() + top_accounts = piv.loc[last_date].dropna().sort_values(ascending=False) + top_rows_html = "" + for rank, (rid, sc) in enumerate(top_accounts.items(), 1): + remapped = "✓" if rid in analytics["ever_remapped"] else "" + bar_w = int(sc / top_accounts.max() * 100) + color = REG_COLORS[(rank - 1) % len(REG_COLORS)] + top_rows_html += f""" + + #{rank} + {rid} + {sc:.6f} + +
+ + {remapped} + """ + + # ───────────────────────────────────────────────────────────── + # HTML TEMPLATE + # ───────────────────────────────────────────────────────────── + html = f""" + + + + +Carmignac Pipeline — Analysis Report + + + + + + +
+
Carmignac × ENSAE · Data Challenge 2025
+

Pipeline Results — Analysis Report

+
Registrar ID repair · Score propagation · Surgery audit
+
+ + +
+
+ Σ score at t_ref + {tl['sum_post'].iloc[-1]:.4f} + post-surgery +
+
+ Σ score at t_min + {tl['sum_post'].iloc[0]:.4f} + post-surgery +
+
+ Max recovery + {tl['recovery_pct'].max():.1f}% + score rescued by surgery +
+
+ Total surgeries + {len(surgery)} + operations performed +
+
+ Reg IDs universe + {piv.shape[1]} + at reference date +
+
+ Ever remapped + {len(analytics['ever_remapped'])} + reg IDs w/ code change +
+
+ + +
+ +
01 · Score Integrity Over Time
+ + +
+
+ Sum of scores — pre vs post surgery + + Post-surgery (solid) shows the corrected score after code repairs. + Pre-surgery (dashed) is the counterfactual without any remapping. + Gap = score rescued. + +
+
+
+ +
+
+
+ + +
+
+
+ Score recovered by surgery + Difference post − pre at each date +
+
+
+ +
+
+
+ +
+
+ Portfolio concentration (entropy) + Shannon entropy of score distribution — higher = more spread +
+
+
+ +
+
+
+
+ +
02 · Individual Score Trajectories
+ +
+
+ Score per Registrar Account — full history + + Dashed lines = accounts that were remapped at some point (surgery applied). + Solid lines = stable codes throughout. + +
+
+
+ +
+
+
+ +
03 · Surgery Operations
+ +
+
+
+ Surgeries per time step + Number of code remappings performed at each month +
+
+
+ +
+
+
+ +
+
+ Score gain per surgery + Average gain in Σ score from surgery at each month +
+
+
+ +
+
+
+
+ +
+
+ Jaccard similarity of surgery matches + + Composite Jaccard score of the matched code pair — closer to 1.0 = stronger portfolio overlap. + Low values may indicate uncertain matches. + +
+
+
+ +
+
+
+ +
04 · Surgery Detail Log
+ +
+
+ All surgery operations +
+
+ {'
No surgeries were performed on this dataset.
' if len(surgery) == 0 else f""" + + + + + + + + + + + + + + {surg_rows_html} +
DateReg origCode fromCode toJaccardScore gain% of score
"""} +
+
+ +
05 · Score Ranking at t_ref
+ +
+
+ Accounts ranked by weight at reference date + ✓ in last column = account was remapped at some point in history +
+
+ + + + + + + + + + + {top_rows_html} +
RankRegistrar IDScore (weight)Relative sizeRemapped
+
+
+ +
+ +
Generated by carmignac_analysis.py · Carmignac × ENSAE Data Challenge 2025
+ + + + +""" + + return html + + +# ───────────────────────────────────────────────────────────── +# 5. MAIN +# ───────────────────────────────────────────────────────────── + +def main(): + parser = argparse.ArgumentParser(description="Carmignac pipeline results analyser") + parser.add_argument("--scores", default="repair_results/carmignac_scores.csv") + parser.add_argument("--mapping", default="repair_results/carmignac_mapping.csv") + parser.add_argument("--surgery", default="repair_results/carmignac_surgery_log.csv") + parser.add_argument("--out", default="repair_results/carmignac_report.html") + args = parser.parse_args() + + # Resolve paths relative to this script's directory if files not found + base = os.path.dirname(os.path.abspath(__file__)) + def resolve(p): + if os.path.exists(p): + return p + alt = os.path.join(base, p) + if os.path.exists(alt): + return alt + sys.exit(f"[ERROR] File not found: {p}") + + scores_path = resolve(args.scores) + mapping_path = resolve(args.mapping) + surgery_path = resolve(args.surgery) + + print(f"[Load] scores : {scores_path}") + print(f"[Load] mapping : {mapping_path}") + print(f"[Load] surgery : {surgery_path}") + + scores, mapping, surgery = load_outputs(scores_path, mapping_path, surgery_path) + analytics = compute_analytics(scores, mapping, surgery) + + print_summary(analytics, surgery) + + html = build_html(analytics, surgery, scores, mapping) + + out_path = args.out + with open(out_path, "w", encoding="utf-8") as f: + f.write(html) + print(f"\n[Report] Written to → {out_path}") + + +if __name__ == "__main__": + main() diff --git a/repair_challenge/carmignac_analysis.py b/repair_challenge/carmignac_analysis.py new file mode 100644 index 0000000..b9b9151 --- /dev/null +++ b/repair_challenge/carmignac_analysis.py @@ -0,0 +1,1351 @@ +""" +Carmignac Data Challenge — Pipeline Results Analysis +===================================================== +Analyses the CSV outputs produced by carmignac_repair.py: + - carmignac_scores.csv (post-surgery score history) + - carmignac_mapping.csv (reg_id mapping history) + - carmignac_surgery_log.csv (surgery operations) + +Produces a self-contained HTML report with interactive charts. + +Usage: + python carmignac_analysis.py + python carmignac_analysis.py --scores path/to/scores.csv \ + --mapping path/to/mapping.csv \ + --surgery path/to/surgery_log.csv \ + --out report.html +""" + +import argparse +import json +import os +import sys +import numpy as np +import pandas as pd + +# ───────────────────────────────────────────────────────────── +# 1. LOAD & VALIDATE +# ───────────────────────────────────────────────────────────── + +def load_outputs(scores_path, mapping_path, surgery_path): + scores = pd.read_csv(scores_path, parse_dates=["date"]) + mapping = pd.read_csv(mapping_path, parse_dates=["date"]) + surgery = pd.read_csv(surgery_path, parse_dates=["date"]) + + # Normalise dtypes + scores["reg_id"] = scores["reg_id"].astype(str) + mapping["reg_orig"] = mapping["reg_orig"].astype(str) + mapping["reg_used"] = mapping["reg_used"].astype(str) + mapping["changed"] = mapping["changed"].astype(bool) + surgery["reg_orig"] = surgery["reg_orig"].astype(str) + surgery["reg_from"] = surgery["reg_from"].astype(str) + surgery["reg_to"] = surgery["reg_to"].astype(str) + + return scores, mapping, surgery + + +# ───────────────────────────────────────────────────────────── +# 2. COMPUTE ANALYTICS +# ───────────────────────────────────────────────────────────── + +def compute_analytics(scores, mapping, surgery): + dates = sorted(scores["date"].unique()) + + # ── 2.1 Sum of scores per date (post-surgery) ────────────── + sum_post = (scores.groupby("date")["score"] + .sum() + .reindex(dates) + .rename("sum_post")) + + # ── 2.2 Reconstruct pre-surgery (counterfactual) ─────────── + # Without surgery, every reg_id that had a hard break would score 0 + # from that date backwards. We propagate the surgery "gain" as a + # cumulative deficit going back in time. + gain_by_date = surgery.groupby("date")["gain_vs_no_surgery"].sum() + # cumulative deficit = sum of gains for all surgeries at or after date t + cumulative_deficit = pd.Series(0.0, index=dates) + for d in dates: + cumulative_deficit[d] = gain_by_date[gain_by_date.index >= d].sum() + sum_pre = (sum_post - cumulative_deficit).clip(lower=0).rename("sum_pre") + + timeline = pd.DataFrame({"sum_post": sum_post, "sum_pre": sum_pre}) + timeline.index = pd.to_datetime(timeline.index) + timeline["recovery_pct"] = np.where( + sum_pre < sum_post, + (sum_post - sum_pre) / sum_post.clip(lower=1e-9) * 100, + 0.0, + ) + + # ── 2.3 Per-date surgery stats ───────────────────────────── + surgery_stats = ( + surgery.groupby("date") + .agg( + n_surgeries = ("reg_orig", "count"), + total_gain = ("gain_vs_no_surgery", "sum"), + avg_gain = ("gain_vs_no_surgery", "mean"), + avg_jaccard = ("jaccard_composite", "mean"), + avg_score_before = ("score_before", "mean"), + avg_score_after = ("score_after", "mean"), + ) + .reindex(dates, fill_value=0) + ) + + # ── 2.4 Score distribution over time ─────────────────────── + # Wide format: rows=dates, cols=reg_ids + pivot = scores.pivot_table(index="date", columns="reg_id", + values="score", aggfunc="last") + pivot = pivot.reindex(dates) + pivot.index = pd.to_datetime(pivot.index) + + # ── 2.5 Mapping churn ────────────────────────────────────── + # For each date, how many reg_ids are remapped (not using their original code)? + churn = (mapping.groupby("date")["changed"] + .sum() + .reindex(dates, fill_value=0) + .rename("n_remapped")) + + # ── 2.6 Score entropy (distribution spread) ──────────────── + def entropy(row): + p = row.dropna() + p = p[p > 0] + if len(p) == 0: + return np.nan + p = p / p.sum() + return -(p * np.log(p)).sum() + + timeline["entropy"] = pivot.apply(entropy, axis=1).values + + # ── 2.7 Individual score trajectories ────────────────────── + # Identify which reg_ids were ever remapped + ever_remapped = set(mapping.loc[mapping["changed"], "reg_orig"].unique()) + + # ── 2.8 Surgery detail table ─────────────────────────────── + surgery_detail = surgery.copy() + surgery_detail["gain_pct_of_score"] = ( + surgery_detail["gain_vs_no_surgery"] + / surgery_detail["score_before"].clip(lower=1e-9) * 100 + ).round(2) + + return { + "timeline": timeline, + "surgery_stats": surgery_stats, + "pivot": pivot, + "churn": churn, + "ever_remapped": ever_remapped, + "surgery_detail": surgery_detail, + "dates": [d.strftime("%Y-%m-%d") for d in dates], + } + + +# ───────────────────────────────────────────────────────────── +# 3. PRINT CONSOLE SUMMARY +# ───────────────────────────────────────────────────────────── + +def print_summary(analytics, surgery): + tl = analytics["timeline"] + ss = analytics["surgery_stats"] + + print("\n" + "=" * 65) + print(" CARMIGNAC PIPELINE — RESULTS SUMMARY") + print("=" * 65) + + print(f"\n Date range : {tl.index.min().date()} → {tl.index.max().date()}") + print(f" Total months : {len(tl)}") + print(f" Reg IDs : {analytics['pivot'].shape[1]}") + + print(f"\n ── Score (Σ) ──────────────────────────────────────────") + print(f" At t_ref (latest) : {tl['sum_post'].iloc[-1]:.6f}") + print(f" At t_min (earliest): {tl['sum_post'].iloc[0]:.6f}") + print(f" Min (post-surgery) : {tl['sum_post'].min():.6f} " + f"({tl['sum_post'].idxmin().date()})") + print(f" Min (pre-surgery) : {tl['sum_pre'].min():.6f} " + f"({tl['sum_pre'].idxmin().date()})") + print(f" Max recovery (pct) : {tl['recovery_pct'].max():.2f}%") + + print(f"\n ── Surgeries ─────────────────────────────────────────") + if len(surgery) == 0: + print(" No surgeries performed.") + else: + print(f" Total operations : {len(surgery)}") + print(f" Total score gained : {surgery['gain_vs_no_surgery'].sum():.6f}") + print(f" Avg Jaccard : {surgery['jaccard_composite'].mean():.4f}") + print(f" Avg gain / surgery : {surgery['gain_vs_no_surgery'].mean():.6f}") + print() + print(f" {'Date':12s} {'Reg orig':12s} {'From':15s} {'To':15s} " + f"{'Jaccard':>8s} {'Gain':>10s}") + print(" " + "-" * 78) + for _, row in surgery.sort_values("date").iterrows(): + print(f" {str(row['date'].date()):12s} {row['reg_orig']:12s} " + f"{row['reg_from']:15s} {row['reg_to']:15s} " + f"{row['jaccard_composite']:8.4f} {row['gain_vs_no_surgery']:10.6f}") + + print(f"\n ── Mapping churn ─────────────────────────────────────") + ch = analytics["churn"] + print(f" Max remapped at one date : {int(ch.max())} ({ch.idxmax().date() if ch.max()>0 else 'N/A'})") + print(f" Reg IDs ever remapped : {len(analytics['ever_remapped'])}") + + print(f"\n ── Score entropy (distribution spread) ───────────────") + ent = analytics["timeline"]["entropy"] + print(f" Mean entropy : {ent.mean():.4f}") + print(f" Std entropy : {ent.std():.4f}") + print() + + +# ───────────────────────────────────────────────────────────── +# 4. BUILD HTML REPORT +# ───────────────────────────────────────────────────────────── + +def build_html(analytics, surgery, scores, mapping): + tl = analytics["timeline"] + ss = analytics["surgery_stats"] + piv = analytics["pivot"] + ch = analytics["churn"] + dates_str = analytics["dates"] + + # ── helpers to serialise for JS ───────────────────────────── + def jf(arr, decimals=6): + return json.dumps([round(float(v), decimals) if not np.isnan(v) else None + for v in arr]) + + def js(arr): + return json.dumps(list(arr)) + + # ── colour palette ─────────────────────────────────────────── + REG_COLORS = [ + "#2563eb","#16a34a","#dc2626","#d97706","#7c3aed", + "#0891b2","#db2777","#65a30d","#ea580c","#6366f1", + "#059669","#b45309","#9333ea","#0284c7","#e11d48", + ] + + # ── 4.1 Surgery sparkline data ────────────────────────────── + surg_dates = [d.strftime("%Y-%m-%d") for d in ss.index] + n_surg = jf(ss["n_surgeries"].values, 0) + total_gain = jf(ss["total_gain"].values) + avg_gain = jf(ss["avg_gain"].values) + avg_jaccard = jf(ss["avg_jaccard"].values) + + # ── 4.2 Individual trajectories ──────────────────────────── + reg_ids = list(piv.columns) + traj_datasets = [] + # Surgery lookup: reg_orig -> list of {date, from, to, composite} + surg_by_reg = {} + for _, row in surgery.iterrows(): + surg_by_reg.setdefault(row["reg_orig"], []).append({ + "date": row["date"].strftime("%Y-%m-%d"), + "reg_from": str(row["reg_from"]), + "reg_to": str(row["reg_to"]), + "composite": round(float(row["jaccard_composite"]), 4), + "gain": round(float(row["gain_vs_no_surgery"]), 6), + }) + + for idx, rid in enumerate(reg_ids): + remapped = rid in analytics["ever_remapped"] + traj_datasets.append({ + "label": rid, + "data": [round(float(v), 6) if not np.isnan(v) else None + for v in piv[rid].values], + "borderColor": REG_COLORS[idx % len(REG_COLORS)], + "backgroundColor": REG_COLORS[idx % len(REG_COLORS)] + "22", + "borderWidth": 2, + "borderDash": [6, 3] if remapped else [], + "pointRadius": 0, + "tension": 0.3, + "fill": False, + "remapped": remapped, + "surgeries": surg_by_reg.get(rid, []), + }) + + traj_json = json.dumps(traj_datasets) + + # ── 4.3 Surgery detail table rows ────────────────────────── + sd = analytics["surgery_detail"].sort_values("date") + surg_rows_html = "" + if len(sd) == 0: + surg_rows_html = "No surgeries performed" + else: + for _, r in sd.iterrows(): + gain_class = "gain-high" if r["gain_vs_no_surgery"] > 0.05 else "gain-low" + surg_rows_html += f""" + + {r['date'].date()} + {r['reg_orig']} + {r['reg_from']} + → + {r['reg_to']} + {r['jaccard_composite']:.4f} + +{r['gain_vs_no_surgery']:.6f} + {r['gain_pct_of_score']:.1f}% + """ + + # ── 4.4 Top accounts table ────────────────────────────────── + last_date = piv.index.max() + top_accounts = piv.loc[last_date].dropna().sort_values(ascending=False) + top_rows_html = "" + for rank, (rid, sc) in enumerate(top_accounts.items(), 1): + remapped = "✓" if rid in analytics["ever_remapped"] else "" + bar_w = int(sc / top_accounts.max() * 100) + color = REG_COLORS[(rank - 1) % len(REG_COLORS)] + top_rows_html += f""" + + #{rank} + {rid} + {sc:.6f} + +
+ + {remapped} + """ + + # ───────────────────────────────────────────────────────────── + # HTML TEMPLATE + # ───────────────────────────────────────────────────────────── + html = f""" + + + + +Carmignac Pipeline — Analysis Report + + + + + + +
+
Carmignac × ENSAE · Data Challenge 2025
+

Pipeline Results — Analysis Report

+
Registrar ID repair · Score propagation · Surgery audit
+
+ + +
+
+ Σ score at t_ref + {tl['sum_post'].iloc[-1]:.4f} + post-surgery +
+
+ Σ score at t_min + {tl['sum_post'].iloc[0]:.4f} + post-surgery +
+
+ Max recovery + {tl['recovery_pct'].max():.1f}% + score rescued by surgery +
+
+ Total surgeries + {len(surgery)} + operations performed +
+
+ Reg IDs universe + {piv.shape[1]} + at reference date +
+
+ Ever remapped + {len(analytics['ever_remapped'])} + reg IDs w/ code change +
+
+ + +
+ +
01 · Score Integrity Over Time
+ + +
+
+ Sum of scores — pre vs post surgery + + Post-surgery (solid) shows the corrected score after code repairs. + Pre-surgery (dashed) is the counterfactual without any remapping. + Gap = score rescued. + +
+
+
+ +
+
+
+ + +
+
+
+ Score recovered by surgery + Difference post − pre at each date +
+
+
+ +
+
+
+ +
+
+ Portfolio concentration (entropy) + Shannon entropy of score distribution — higher = more spread +
+
+
+ +
+
+
+
+ +
02 · Individual Score Trajectories
+ +
+
+ Score explorer — per Registrar Account + + Click an account to inspect its full history. + ◆ remapped = surgery was applied. + +
+
+ + +
+ + +
+ +
+ +
+
+ + +
All accounts — overview
+
+ +
+
+ +
03 · Surgery Operations
+ +
+
+
+ Surgeries per time step + Number of code remappings performed at each month +
+
+
+ +
+
+
+ +
+
+ Score gain per surgery + Average gain in Σ score from surgery at each month +
+
+
+ +
+
+
+
+ +
+
+ Jaccard similarity of surgery matches + + Composite Jaccard score of the matched code pair — closer to 1.0 = stronger portfolio overlap. + Low values may indicate uncertain matches. + +
+
+
+ +
+
+
+ +
04 · Surgery Detail Log
+ +
+
+ All surgery operations +
+
+ {'
No surgeries were performed on this dataset.
' if len(surgery) == 0 else f""" + + + + + + + + + + + + + + {surg_rows_html} +
DateReg origCode fromCode toJaccardScore gain% of score
"""} +
+
+ +
05 · Score Ranking at t_ref
+ +
+
+ Accounts ranked by weight at reference date + ✓ in last column = account was remapped at some point in history +
+
+ + + + + + + + + + + {top_rows_html} +
RankRegistrar IDScore (weight)Relative sizeRemapped
+
+
+ +
+ +
Generated by carmignac_analysis.py · Carmignac × ENSAE Data Challenge 2025
+ + + + +""" + + return html + + +# ───────────────────────────────────────────────────────────── +# 5. MAIN +# ───────────────────────────────────────────────────────────── + +def main(): + parser = argparse.ArgumentParser(description="Carmignac pipeline results analyser") + parser.add_argument("--scores", default="repair_results/carmignac_scores.csv") + parser.add_argument("--mapping", default="repair_results/carmignac_mapping.csv") + parser.add_argument("--surgery", default="repair_results/carmignac_surgery_log.csv") + parser.add_argument("--out", default="repair_results/carmignac_report.html") + args = parser.parse_args() + + # Resolve paths relative to this script's directory if files not found + base = os.path.dirname(os.path.abspath(__file__)) + def resolve(p): + if os.path.exists(p): + return p + alt = os.path.join(base, p) + if os.path.exists(alt): + return alt + sys.exit(f"[ERROR] File not found: {p}") + + scores_path = resolve(args.scores) + mapping_path = resolve(args.mapping) + surgery_path = resolve(args.surgery) + + print(f"[Load] scores : {scores_path}") + print(f"[Load] mapping : {mapping_path}") + print(f"[Load] surgery : {surgery_path}") + + scores, mapping, surgery = load_outputs(scores_path, mapping_path, surgery_path) + analytics = compute_analytics(scores, mapping, surgery) + + print_summary(analytics, surgery) + + html = build_html(analytics, surgery, scores, mapping) + + out_path = args.out + with open(out_path, "w", encoding="utf-8") as f: + f.write(html) + print(f"\n[Report] Written to → {out_path}") + + +if __name__ == "__main__": + main() diff --git a/repair_challenge/carmignac_repair.py b/repair_challenge/carmignac_repair.py deleted file mode 100644 index 873ef47..0000000 --- a/repair_challenge/carmignac_repair.py +++ /dev/null @@ -1,682 +0,0 @@ -""" -Carmignac Data Challenge — Registrar ID Repair Pipeline -========================================================= -Étape 1 : Filtrage & univers de référence à t=31/10/2025 -Étape 2 : Score de cohérence temporelle (propagation vers le passé) -Étape 3 : Chirurgie de code (matching 1-to-1) -""" - -import pandas as pd -import numpy as np -from collections import defaultdict -import os -import s3fs - -# ───────────────────────────────────────────── -# PARAMÈTRES -# ───────────────────────────────────────────── -ALPHA = 0.01 # tolérance réconciliation : 1% du stock à t -MIN_AUM_EUR = 5e6 # seuil filtrage étape 1 -MIN_JACCARD = 0.3 # seuil minimal similarité portefeuille pour chirurgie -SCORE_DROP_THRESHOLD = 0.5 # si score chute de >50% → candidat chirurgie - -EXCLUDE_REGISTRAR = ["Off Distribution", "Private Clients"] - -# ───────────────────────────────────────────── -# 1. CHARGEMENT -# ───────────────────────────────────────────── -def connect_s3fs(): - 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"]) - return fs - -def load_data(aum_path, flows_path): - fs = connect_s3fs() - - with fs.open(aum_path, 'rb') as aum_raw: - aum = pd.read_csv(aum_raw, sep=";") - - with fs.open(flows_path, 'rb') as flows_raw: - flows = pd.read_csv(flows_raw, sep=";") - - aum['Centralisation Date'] = pd.to_datetime(aum['Centralisation Date']) - flows['Centralisation Date'] = pd.to_datetime(flows['Centralisation Date']) - - aum = aum.rename(columns={ - 'Registrar Account - ID': 'reg_id', - 'Product - Isin': 'isin', - 'Centralisation Date': 'date', - 'Quantity - AUM': 'qty_aum', - 'Value - AUM €': 'val_eur', - 'Registrar Account - Region': 'region', - }) - flows = flows.rename(columns={ - 'Registrar Account - ID': 'reg_id', - 'Product - Isin': 'isin', - 'Centralisation Date': 'date', - 'Quantity - NetFlows': 'qty_net', - 'Value € - NetFlows': 'val_net_eur', - }) - - aum['reg_id'] = aum['reg_id'].astype(str) - flows['reg_id'] = flows['reg_id'].astype(str) - - return aum, flows - -# ───────────────────────────────────────────── -# 2. ÉTAPE 1 — Univers de référence à T_REF -# ───────────────────────────────────────────── -def build_reference_universe(aum, t_ref=None): - """ - Construit l'univers de référence à t_ref (dernière date par défaut). - Retourne : - - aum_ref : AUM à t_ref pour chaque (reg_id, isin) - - weights : poids normalisé par reg_id - - universe : ensemble des reg_id retenus (>= MIN_AUM_EUR) - """ - if t_ref is None: - t_ref = aum['date'].max() - - print(f"\n[Étape 1] Date de référence : {t_ref.date()}") - - # Exclure Off Distribution / Private Clients (sur région ou nom) - mask_excl = aum['reg_id'].isin(EXCLUDE_REGISTRAR) - if 'region' in aum.columns: - mask_excl |= aum['region'].isin(EXCLUDE_REGISTRAR) - aum_clean = aum[~mask_excl].copy() - - # AUM à t_ref - aum_ref = aum_clean[aum_clean['date'] == t_ref][['reg_id', 'isin', 'qty_aum', 'val_eur']].copy() - - # AUM total par reg_id à t_ref - aum_by_reg = aum_ref.groupby('reg_id')['val_eur'].sum().rename('total_eur') - - # Filtrage >= MIN_AUM_EUR - universe = set(aum_by_reg[aum_by_reg >= MIN_AUM_EUR].index) - - total_eur_universe = aum_by_reg[aum_by_reg.index.isin(universe)].sum() - total_eur_all = aum_by_reg.sum() - coverage = total_eur_universe / total_eur_all if total_eur_all > 0 else 0 - - print(f" Registrar IDs à t_ref : {len(aum_by_reg)}") - print(f" Dont >= {MIN_AUM_EUR/1e6:.0f}M€ : {len(universe)}") - print(f" Couverture encours : {coverage:.1%}") - - # Poids initiaux (scores à t_ref) - weights = (aum_by_reg[aum_by_reg.index.isin(universe)] / total_eur_universe).to_dict() - - return aum_ref, weights, universe, t_ref - -# ───────────────────────────────────────────── -# 3. PANEL AUM MENSUEL (forward-fill) -# ───────────────────────────────────────────── -def build_monthly_panel(aum, universe, t_ref): - """ - Construit un panel mensuel complet (forward-fill des quantités AUM) - pour TOUS les reg_ids présents dans l'historique AUM — y compris les codes - historiques hors univers de référence, nécessaires pour la chirurgie. - """ - # Toutes les fin de mois entre la première date et t_ref - date_min = aum['date'].min() - all_months = pd.date_range(start=date_min, end=t_ref, freq='ME') - - # Pivot : (reg_id, isin) → série temporelle de qty_aum - aum_sorted = aum.sort_values(['reg_id', 'isin', 'date']) - - # On ne garde que les lignes jusqu'à t_ref - aum_sorted = aum_sorted[aum_sorted['date'] <= t_ref] - - # Multi-index pivot - panel = aum_sorted.pivot_table( - index='date', columns=['reg_id', 'isin'], values='qty_aum', aggfunc='last' - ) - - # Réindexer sur toutes les fins de mois - panel = panel.reindex(all_months) - - # Forward-fill : si pas de mouvement, la quantité reste la même - panel = panel.ffill() - - # Backward-fill initial pour les comptes qui démarrent après la première date - # (on ne remonte pas avant leur première apparition → on garde NaN) - - print(f"\n[Panel mensuel] {len(all_months)} mois, {panel.shape[1]} (reg_id, isin) paires") - - return panel, all_months - -# ───────────────────────────────────────────── -# 4. FLOWS AGRÉGÉS PAR MOIS -# ───────────────────────────────────────────── -def aggregate_flows_monthly(flows, all_months): - """ - Agrège les flows infra-mensuels sur chaque fenêtre ]fin_mois(t-1), fin_mois(t)]. - Retourne un DataFrame indexé par (fin_mois, reg_id, isin). - """ - flows_f = flows[flows['date'] <= all_months[-1]].copy() - - # Associer chaque transaction à la fin de mois correspondante - # = la première fin de mois >= date de transaction - flows_f['month_end'] = flows_f['date'].apply( - lambda d: all_months[all_months >= d][0] if any(all_months >= d) else pd.NaT - ) - flows_f = flows_f.dropna(subset=['month_end']) - - # Agrégation - monthly_flows = flows_f.groupby(['month_end', 'reg_id', 'isin'])['qty_net'].sum() - monthly_flows = monthly_flows.reset_index() - monthly_flows.columns = ['date', 'reg_id', 'isin', 'qty_net_month'] - - print(f"\n[Flows mensuels] {len(monthly_flows)} enregistrements (reg_id, isin, mois)") - - return monthly_flows - -# ───────────────────────────────────────────── -# 5. ÉTAPE 2 — Score de cohérence temporelle -# ───────────────────────────────────────────── -def compute_reconciliation_error(qty_t_minus1, qty_t, net_flow, alpha=ALPHA): - """ - Calcule l'erreur de réconciliation normalisée pour un (reg_id, isin, mois). - - Attendu : qty_t_minus1 + net_flow ≈ qty_t - Erreur : |qty_t_minus1 + net_flow - qty_t| / max(|qty_t|, |qty_t_minus1|) - - Retourne : - - err_ratio : erreur relative (0 = parfait) - - is_break : True si err_ratio > alpha - """ - denom = max(abs(qty_t), abs(qty_t_minus1), 1e-9) - err = abs(qty_t_minus1 + net_flow - qty_t) - err_ratio = err / denom - return err_ratio, err_ratio > alpha - -def score_propagation(panel, monthly_flows, weights, universe, all_months): - """ - Propage les scores de t_ref vers t=0 (passé). - - À chaque mois t (en remontant), pour chaque reg_id dans l'univers courant : - - Calculer l'erreur de réconciliation pondérée par ISIN - - Dégrader le score proportionnellement - - Retourne : - - scores_history : dict {date → {reg_id → score}} - - errors_history : dict {date → {reg_id → err_pondérée}} - - mapping : dict {reg_id_original → reg_id_courant} (après chirurgie) - """ - # Initialisation - scores = dict(weights) # scores à t_ref - scores_history = {all_months[-1]: dict(scores)} - errors_history = {} - - # Mapping actuel (identité au départ) - mapping = {r: r for r in universe} - - # Flows indexés pour accès rapide - flows_idx = monthly_flows.set_index(['date', 'reg_id', 'isin'])['qty_net_month'] - - # Remonter dans le temps - for i in range(len(all_months) - 2, -1, -1): - t_prev = all_months[i] - t_curr = all_months[i + 1] - - errors_at_t = {} - new_scores = {} - - for reg_orig, reg_curr in mapping.items(): - score_curr = scores.get(reg_orig, 0) - if score_curr == 0: - new_scores[reg_orig] = 0 - continue - - # ISIN détenus par ce reg à t_curr (après mapping) - if reg_curr in panel.columns.get_level_values(0): - isin_list = panel[reg_curr].columns.tolist() - else: - # reg_curr n'existe pas du tout dans le panel → rupture totale - new_scores[reg_orig] = 0 - errors_at_t[reg_orig] = 1.0 - continue - - total_aum_t = 0 - weighted_err = 0 - valid_isin_count = 0 - all_nan_at_prev = True # détecte si le compte n'existait pas à t_prev - - for isin in isin_list: - qty_t = panel[reg_curr][isin].get(t_curr, np.nan) - qty_t_prev = panel[reg_curr][isin].get(t_prev, np.nan) - - if pd.isna(qty_t): - continue - - if not pd.isna(qty_t_prev): - all_nan_at_prev = False - - if pd.isna(qty_t_prev): - # ISIN existait à t_curr mais pas à t_prev → rupture sur cet ISIN - # On le traite comme une erreur maximale pondérée par son AUM - weight_isin = abs(qty_t) - weighted_err += 1.0 * weight_isin - total_aum_t += weight_isin - valid_isin_count += 1 - continue - - if qty_t == 0 and qty_t_prev == 0: - continue - # Flow agrégé sur ]t_prev, t_curr] - try: - net_flow = flows_idx.loc[(t_curr, reg_curr, isin)] - except KeyError: - net_flow = 0.0 - - err_ratio, is_break = compute_reconciliation_error( - qty_t_prev, qty_t, net_flow, alpha=ALPHA - ) - - # Pondération par AUM à t_curr - weight_isin = abs(qty_t) - weighted_err += err_ratio * weight_isin - total_aum_t += weight_isin - valid_isin_count += 1 - - if total_aum_t > 0 and valid_isin_count > 0: - avg_err = weighted_err / total_aum_t - else: - avg_err = 0.0 - - errors_at_t[reg_orig] = avg_err - - # Dégradation du score : score(t-1) = score(t) * (1 - err_pondérée) - # Clippée entre 0 et score_curr - degradation = min(avg_err, 1.0) - new_scores[reg_orig] = score_curr * (1.0 - degradation) - - scores = new_scores - scores_history[t_prev] = dict(scores) - errors_history[t_prev] = dict(errors_at_t) - - total_score = sum(scores.values()) - print(f" {t_prev.date()} | Σ scores = {total_score:.4f} | " - f"Comptes actifs = {sum(1 for v in scores.values() if v > 0)}") - - return scores_history, errors_history, mapping - -# ───────────────────────────────────────────── -# 6. ÉTAPE 3 — Chirurgie de code -# ───────────────────────────────────────────── -def jaccard_isin(set_a, set_b): - """Coefficient de Jaccard entre deux ensembles d'ISIN.""" - if not set_a or not set_b: - return 0.0 - inter = len(set_a & set_b) - union = len(set_a | set_b) - return inter / union if union > 0 else 0.0 - -def find_best_candidate(reg_orig, reg_curr, t_prev, t_curr, - panel, flows_idx, all_regs_at_t_prev, mapping_inv): - """ - Pour un reg_id dont le score a fortement chuté, cherche le meilleur - candidat j à t_prev tel que : - - j n'est pas déjà mappé à un autre compte original - - Le portefeuille ISIN de j à t_prev est similaire à celui de reg_curr à t_curr - - La réconciliation est bonne - - Retourne (best_candidate, best_score_composite) ou (None, 0) - """ - # ISIN du compte cible à t_curr - if reg_curr not in panel.columns.get_level_values(0): - return None, 0.0 - - isin_curr = set(panel[reg_curr].columns[ - panel[reg_curr].loc[t_curr].notna() & (panel[reg_curr].loc[t_curr] != 0) - ].tolist()) - - if not isin_curr: - return None, 0.0 - - best_candidate = None - best_composite = 0.0 - - for j in all_regs_at_t_prev: - # Ne pas réutiliser un code déjà mappé - if j in mapping_inv: - continue - # Ne pas mapper sur soi-même si déjà présent - if j == reg_curr: - continue - - if j not in panel.columns.get_level_values(0): - continue - - # ISIN de j à t_prev - col_j = panel[j] - isin_j = set(col_j.columns[ - col_j.loc[t_prev].notna() & (col_j.loc[t_prev] != 0) - ].tolist()) if t_prev in col_j.index else set() - - if not isin_j: - continue - - jac = jaccard_isin(isin_curr, isin_j) - if jac < MIN_JACCARD: - continue - - # Erreur de réconciliation pour les ISIN communs - common_isin = isin_curr & isin_j - total_aum = 0 - weighted_err = 0 - - for isin in common_isin: - qty_t = panel[reg_curr][isin].get(t_curr, np.nan) if isin in panel[reg_curr].columns else np.nan - qty_t_prev = panel[j][isin].get(t_prev, np.nan) if isin in panel[j].columns else np.nan - - if pd.isna(qty_t) or pd.isna(qty_t_prev): - continue - - try: - net_flow = flows_idx.loc[(t_curr, j, isin)] - except KeyError: - net_flow = 0.0 - - err_ratio, _ = compute_reconciliation_error(qty_t_prev, qty_t, net_flow) - weight_isin = abs(qty_t) - weighted_err += err_ratio * weight_isin - total_aum += weight_isin - - avg_err = weighted_err / total_aum if total_aum > 0 else 1.0 - - composite = jac * (1.0 - min(avg_err, 1.0)) - - if composite > best_composite: - best_composite = composite - best_candidate = j - - return best_candidate, best_composite - - -def _recompute_score_with_candidate(reg_orig, candidate, t_prev, t_curr, - panel, flows_idx, score_curr): - """ - Recalcule proprement l'erreur de réconciliation pour un candidat donné, - et retourne le score après chirurgie. - """ - if candidate not in panel.columns.get_level_values(0): - return score_curr * 0 # candidat inexistant - - isin_list_cand = panel[candidate].columns.tolist() - isin_list_curr = panel[reg_orig].columns.tolist() if reg_orig in panel.columns.get_level_values(0) else [] - - total_aum = 0 - weighted_err = 0 - - for isin in isin_list_curr: - qty_t = panel[reg_orig][isin].get(t_curr, np.nan) if isin in panel[reg_orig].columns else np.nan - if pd.isna(qty_t) or qty_t == 0: - continue - - qty_t_prev = panel[candidate][isin].get(t_prev, np.nan) if isin in panel[candidate].columns else np.nan - - try: - net_flow = flows_idx.loc[(t_curr, candidate, isin)] - except KeyError: - net_flow = 0.0 - - if pd.isna(qty_t_prev): - err_ratio = 1.0 - else: - err_ratio, _ = compute_reconciliation_error(qty_t_prev, qty_t, net_flow) - - weight_isin = abs(qty_t) - weighted_err += err_ratio * weight_isin - total_aum += weight_isin - - avg_err = weighted_err / total_aum if total_aum > 0 else 1.0 - return score_curr * (1.0 - min(avg_err, 1.0)) - - -def run_surgery_pass(scores_history, errors_history, panel, monthly_flows, - weights, universe, all_months): - """ - Deuxième passe : pour chaque mois avec des ruptures fortes, - tente une chirurgie de code et recalcule les scores. - - Corrections par rapport à la passe naïve : - - Après chirurgie, le score est recalculé proprement (pas juste composite) - - Le mapping propagé en arrière utilise le bon code à chaque étape - - Pré-filtre ISIN pour performance sur grand dataset - - Retourne : - - mapping_history : {date → {reg_orig → reg_used}} - - surgery_log : liste des opérations effectuées - - scores_final : scores au dernier mois - """ - flows_idx = monthly_flows.set_index(['date', 'reg_id', 'isin'])['qty_net_month'] - - # Tous les reg_ids présents dans le panel (univers + codes historiques) - all_regs_in_panel = set(panel.columns.get_level_values(0)) - - # Pré-calcul : ensemble d'ISIN par reg_id à chaque date (pour pré-filtre rapide) - # {reg_id → {date → set(isin)}} - reg_isin_at_date = {} - for reg in all_regs_in_panel: - reg_isin_at_date[reg] = {} - col = panel[reg] - for date in col.index: - active = set(col.columns[(col.loc[date].notna()) & (col.loc[date] != 0)].tolist()) - if active: - reg_isin_at_date[reg][date] = active - - # Mapping courant : reg_orig → reg_used - mapping = {r: r for r in universe} - mapping_inv = {r: r for r in universe} - - surgery_log = [] - mapping_history = {all_months[-1]: dict(mapping)} - scores_history_corrected = {all_months[-1]: dict(weights)} - - # Scores courants (initialisés à t_ref) - scores = dict(weights) - - for i in range(len(all_months) - 2, -1, -1): - t_prev = all_months[i] - t_curr = all_months[i + 1] - - new_scores = {} - new_mapping = {} - - for reg_orig in list(mapping.keys()): - reg_curr = mapping[reg_orig] - score_curr = scores.get(reg_orig, 0) - - if score_curr == 0: - new_scores[reg_orig] = 0 - new_mapping[reg_orig] = reg_curr - continue - - # Erreur sans chirurgie (depuis étape 2) - err = errors_history.get(t_prev, {}).get(reg_orig, 0.0) - score_prev_no_surgery = score_curr * (1.0 - min(err, 1.0)) - drop_ratio = 1.0 - (score_prev_no_surgery / score_curr) if score_curr > 0 else 0 - - if drop_ratio > SCORE_DROP_THRESHOLD: - # ── ISIN du compte courant à t_curr (pour pré-filtre) ── - isin_curr = reg_isin_at_date.get(reg_curr, {}).get(t_curr, set()) - - # ── Candidats disponibles (non déjà mappés) ── - available = all_regs_in_panel - set(mapping_inv.keys()) - {reg_curr} - - best_candidate = None - best_score_after = score_prev_no_surgery # baseline = pas de chirurgie - best_composite = 0.0 - - for j in available: - # Pré-filtre rapide : overlap ISIN minimal - isin_j = reg_isin_at_date.get(j, {}).get(t_prev, set()) - if not isin_curr or not isin_j: - continue - inter = len(isin_curr & isin_j) - if inter == 0: - continue - jac = inter / len(isin_curr | isin_j) - if jac < MIN_JACCARD: - continue - - # Score après chirurgie avec ce candidat - score_after = _recompute_score_with_candidate( - reg_curr, j, t_prev, t_curr, panel, flows_idx, score_curr - ) - composite = jac * (score_after / score_curr) if score_curr > 0 else 0 - - if score_after > best_score_after: - best_score_after = score_after - best_candidate = j - best_composite = composite - - if best_candidate: - surgery_log.append({ - 'date': t_prev, - 'reg_orig': reg_orig, - 'reg_from': reg_curr, - 'reg_to': best_candidate, - 'jaccard_composite': round(best_composite, 4), - 'score_before': round(score_curr, 6), - 'score_after': round(best_score_after, 6), - 'drop_without_surgery': round(drop_ratio, 4), - 'gain_vs_no_surgery': round(best_score_after - score_prev_no_surgery, 6), - }) - print(f" 🔧 CHIRURGIE {t_prev.date()} | {reg_orig} : " - f"{reg_curr} → {best_candidate} " - f"(composite={best_composite:.3f}, " - f"score {score_curr:.4f}→{best_score_after:.4f})") - - # Mise à jour mapping - if reg_curr in mapping_inv: - del mapping_inv[reg_curr] - mapping_inv[best_candidate] = reg_orig - new_mapping[reg_orig] = best_candidate - new_scores[reg_orig] = best_score_after - else: - new_mapping[reg_orig] = reg_curr - new_scores[reg_orig] = score_prev_no_surgery - else: - new_mapping[reg_orig] = reg_curr - new_scores[reg_orig] = score_prev_no_surgery - - mapping = new_mapping - mapping_inv = {v: k for k, v in mapping.items()} - scores = new_scores - mapping_history[t_prev] = dict(mapping) - scores_history_corrected[t_prev] = dict(scores) - - total_score = sum(s for s in scores.values() if not np.isnan(s)) - n_surgeries = sum(1 for op in surgery_log if op['date'] == t_prev) - print(f" {t_prev.date()} | Σ scores = {total_score:.4f} | " - f"Chirurgies = {n_surgeries}") - - return mapping_history, surgery_log, scores, scores_history_corrected - -# ───────────────────────────────────────────── -# 7. EXPORT RÉSULTATS -# ───────────────────────────────────────────── -def export_results(scores_history, mapping_history, surgery_log, all_months, out_prefix="carmignac"): - """Exporte les résultats clés en CSV.""" - - # Score history - rows = [] - for date, sc in scores_history.items(): - for reg, score in sc.items(): - rows.append({'date': date, 'reg_id': reg, 'score': score}) - df_scores = pd.DataFrame(rows) if rows else pd.DataFrame(columns=['date', 'reg_id', 'score']) - if not df_scores.empty: - df_scores = df_scores.sort_values(['date', 'score'], ascending=[True, False]) - df_scores.to_csv(f"repair_results/{out_prefix}_scores.csv", index=False) - - # Mapping history - rows_m = [] - for date, mp in mapping_history.items(): - for reg_orig, reg_used in mp.items(): - rows_m.append({'date': date, 'reg_orig': reg_orig, 'reg_used': reg_used, - 'changed': reg_orig != reg_used}) - df_mapping = pd.DataFrame(rows_m) if rows_m else pd.DataFrame(columns=['date', 'reg_orig', 'reg_used', 'changed']) - if not df_mapping.empty: - df_mapping = df_mapping.sort_values(['date', 'reg_orig']) - df_mapping.to_csv(f"repair_results/{out_prefix}_mapping.csv", index=False) - - # Surgery log - if surgery_log: - df_surgery = pd.DataFrame(surgery_log).sort_values('date') - df_surgery.to_csv(f"repair_results/{out_prefix}_surgery_log.csv", index=False) - print(f"\n[Export] {len(surgery_log)} opérations de chirurgie sauvegardées.") - else: - print("\n[Export] Aucune chirurgie effectuée sur ce subset.") - - print(f"[Export] Scores → {out_prefix}_scores.csv") - print(f"[Export] Mapping → {out_prefix}_mapping.csv") - - return df_scores, df_mapping - -# ───────────────────────────────────────────── -# 8. PIPELINE PRINCIPAL -# ───────────────────────────────────────────── -def run_pipeline(aum_path, flows_path): - print("=" * 60) - print("CARMIGNAC — Pipeline de réparation des Registrar IDs") - print("=" * 60) - - # Chargement - aum, flows = load_data(aum_path, flows_path) - - # Étape 1 — Univers de référence - aum_ref, weights, universe, t_ref = build_reference_universe(aum) - - print(f"\n Top 5 comptes par poids :") - for reg, w in sorted(weights.items(), key=lambda x: -x[1])[:5]: - print(f" {reg} : {w:.4f} ({w*100:.2f}%)") - - # Panel mensuel - panel, all_months = build_monthly_panel(aum, universe, t_ref) - - # Flows mensuels agrégés - monthly_flows = aggregate_flows_monthly(flows, all_months) - - # Étape 2 — Score de cohérence (sans chirurgie) - print("\n[Étape 2] Propagation des scores (sans chirurgie)...") - scores_history, errors_history, _ = score_propagation( - panel, monthly_flows, weights, universe, all_months - ) - - # Étape 3 — Chirurgie - print("\n[Étape 3] Passe de chirurgie...") - mapping_history, surgery_log, final_scores, scores_history_corrected = run_surgery_pass( - scores_history, errors_history, panel, monthly_flows, - weights, universe, all_months - ) - - # Export — on utilise les scores corrigés (post-chirurgie) comme référence - print("\n[Export des résultats...]") - df_scores, df_mapping = export_results( - scores_history_corrected, mapping_history, surgery_log, all_months - ) - - # Résumé final - print("\n" + "=" * 60) - print("RÉSUMÉ FINAL") - print("=" * 60) - print(f" Dates couvertes : {all_months[0].date()} → {all_months[-1].date()}") - print(f" Comptes dans l'univers : {len(universe)}") - print(f" Chirurgies effectuées : {len(surgery_log)}") - score_by_date = {d: sum(s for s in sc.values() if s == s) - for d, sc in scores_history_corrected.items()} - print(f" Σ scores à t_ref : {score_by_date[t_ref]:.4f}") - print(f" Σ scores à t_min : {score_by_date[all_months[0]]:.4f}") - - return df_scores, df_mapping, surgery_log, scores_history_corrected, mapping_history - - -if __name__ == "__main__": - df_scores, df_mapping, surgery_log, scores_history, mapping_history = run_pipeline( - "s3://projet-bdc-carmignac-g3/stock_repaired.csv", - "projet-bdc-data//carmignac/Flows ENSAE V2 -20251105.csv" - ) diff --git a/repair_challenge/repair_results/carmignac_report.html b/repair_challenge/repair_results/carmignac_report.html new file mode 100644 index 0000000..bbaa54b --- /dev/null +++ b/repair_challenge/repair_results/carmignac_report.html @@ -0,0 +1,10009 @@ + + + + + +Carmignac Pipeline — Analysis Report + + + + + + +
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Carmignac × ENSAE · Data Challenge 2025
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Pipeline Results — Analysis Report

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Registrar ID repair · Score propagation · Surgery audit
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+ Σ score at t_ref + 1.0000 + post-surgery +
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+ Σ score at t_min + 0.2476 + post-surgery +
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+ Max recovery + 70.5% + score rescued by surgery +
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+ Total surgeries + 512 + operations performed +
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+ Reg IDs universe + 432 + at reference date +
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+ Ever remapped + 340 + reg IDs w/ code change +
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01 · Score Integrity Over Time
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+ Sum of scores — pre vs post surgery + + Post-surgery (solid) shows the corrected score after code repairs. + Pre-surgery (dashed) is the counterfactual without any remapping. + Gap = score rescued. + +
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+ Score recovered by surgery + Difference post − pre at each date +
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+ Portfolio concentration (entropy) + Shannon entropy of score distribution — higher = more spread +
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02 · Individual Score Trajectories
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+ Score explorer — per Registrar Account + + Click an account to inspect its full history. + ◆ remapped = surgery was applied. + +
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All accounts — overview
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03 · Surgery Operations
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+ Surgeries per time step + Number of code remappings performed at each month +
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+ Score gain per surgery + Average gain in Σ score from surgery at each month +
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+ Jaccard similarity of surgery matches + + Composite Jaccard score of the matched code pair — closer to 1.0 = stronger portfolio overlap. + Low values may indicate uncertain matches. + +
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04 · Surgery Detail Log
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+ All surgery operations +
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2019-11-30200127225200127225→4186110.4630+0.00001482.3%
2019-11-30200127417200127417→2000931490.9807+0.00035898.1%
2019-11-30200127308200127307→4222250.6016+0.00001970.4%
2019-11-30200127430200127430→2000903340.6283+0.00275394.2%
2019-11-30200127316200127316→2000900010.5390+0.00492783.3%
2019-11-30200127613200127613→4203750.9976+0.000008100.0%
2019-11-30200127410366582→2001266030.0352+0.0000000.0%
2019-11-30200127552200127552→2000916530.3369+0.00230449.4%
2019-11-30200127587200127587→2000903210.4706+0.00005870.7%
2019-11-30200127306200127306→2000903470.9844+0.00043498.6%
2019-11-30200127598200127598→2001022390.9724+0.000012100.0%
2019-11-30200127186200127186→2001263500.9880+0.00164498.8%
2019-11-30200127174200127174→2000902880.4421+0.00007373.7%
2019-11-30200127484200127484→4060480.9583+0.00012196.0%
2019-11-30200127435200127435→2000979370.9700+0.00020494.9%
2019-11-30200127415200127415→4057600.4258+0.00250559.6%
2019-11-30200127343200001942→4176540.0644+0.0000000.0%
2019-11-30200127191200127191→2000900350.3309+0.00007488.1%
2019-11-30200127454200127454→4163480.9826+0.01399898.3%
2019-11-30200127703364544→4200490.4777+0.00001386.7%
2019-11-30200127433200127433→2001265390.9386+0.00060593.8%
2019-11-30200127403200127403→2001021380.9893+0.00034298.8%
2019-11-30200127391200127391→2000900750.9917+0.00045799.3%
2019-11-30200127371200127371→4228240.1137+0.00000222.2%
2019-11-30200127157200127157→2000903100.4482+0.00002180.8%
2019-11-30200127362200127362→2000900570.9169+0.00189897.8%
2019-11-30200127376200127376→2000899700.9936+0.00043399.3%
2019-11-30200127456200127456→2001017100.2815+0.00001083.3%
2019-11-30200127404200127404→2000900530.5384+0.00080889.8%
2019-11-30200127434200127434→2001268160.9525+0.00001493.3%
2019-11-30200127401200127401→4166300.5000+0.000052100.0%
2019-11-30200127272365348→3628200.1824+0.00007452.9%
2019-11-30200127175200015699→3664120.0590+0.0000000.0%
2019-11-30200127553200127553→4066810.4392+0.00035170.9%
2019-11-30200127605200127605→2000931520.9783+0.00257497.8%
2019-11-30200127419200127419→2000903120.9776+0.00011298.2%
2019-11-30200127383200127383→2000932790.9781+0.00033097.9%
2019-11-30200127455200101868→4160780.2805+0.00000150.0%
2019-11-30200127498200127498→2001021530.9852+0.00001191.7%
2019-11-30200127432200127432→2000019680.8713+0.00217398.0%
2019-11-30200127190200127261→2000903460.6125+0.000001100.0%
2019-11-30200127135200127135→4052970.9854+0.00032698.5%
2019-11-30200127185200127185→2000983030.5408+0.00000375.0%
2019-11-30200127578200127578→2000931530.9749+0.00041497.4%
2019-11-30200127583200127583→2000436610.9858+0.00008598.8%
2019-11-30200127478200001522→2000397650.0966+0.00000320.0%
2019-11-30200127614200127614→4064630.9889+0.00001493.3%
2019-11-30200127463200127463→4063080.7038+0.00012196.8%
2019-11-30200127448200127448→2001019800.9956+0.00037599.7%
2019-11-30200127555200127410→2000899450.1977+0.0000000.0%
2019-11-30200127556200127556→2001022190.9620+0.00027196.1%
2019-11-30200127901200127901→2001279020.8532+0.00012397.6%
2019-11-30200127603200127603→2001265450.9973+0.000212100.0%
2019-11-30200127447200127447→2000240030.9753+0.00012597.7%
2019-11-30200127187200127187→2000900740.0583+0.00010919.4%
2019-11-30200127446200127446→2000900440.4933+0.00093749.3%
2019-11-30200127364200127364→2000900560.4829+0.00047980.5%
2019-11-30200127798200127798→2000022560.9627+0.00024296.0%
2019-12-31200102197362820→3659900.0360+0.0000000.0%
2019-12-31200127733200086500→2001265950.1460+0.00000125.0%
2019-12-31200127803366110→2001021900.3978+0.00006873.9%
2019-12-31200127764200101614→2000022260.9136+0.00037691.3%
2020-01-31200127816200127816→2001271300.4404+0.00021771.8%
2020-01-31200127809200127809→2001275550.0202+0.0004205.0%
2020-01-31200127817200127817→2000014560.1104+0.00000635.3%
2020-01-31200127815200127815→2001274260.1651+0.00063249.6%
2020-01-31200127812200127812→2001274210.2936+0.00000457.1%
2020-01-31200127803200127803→3661100.4959+0.00005742.9%
2020-01-31200095646200002164→2000018600.4739+0.00001794.4%
2020-01-31200127455200127455→2001018680.0909+0.0000019.1%
2020-01-31200127811200127811→621260.2398+0.00044351.9%
2020-01-31200127810200127810→3659220.1419+0.00069128.4%
2020-01-31200127733200127733→2000865000.0733+0.00000315.8%
2020-01-31200127478200039765→2000015220.2409+0.00001553.6%
2020-02-29200127996200127262→2001278190.1547+0.00004746.1%
2020-02-2920010219762126→3628200.3124+0.00000233.3%
2020-02-29200127903420138→3654180.3273+0.00013997.9%
2020-03-31200126515200126515→2000015270.6679+0.00022742.7%
2020-03-31200128481200128481→4222640.2139+0.00009842.8%
2020-03-31200128363200128363→2001277690.9068+0.00030390.7%
2020-03-31200127416200127416→2001274200.9902+0.00291698.8%
2020-03-31200127639200127639→2001274910.9506+0.000002100.0%
2020-03-31200127903200127903→4201380.3627+0.00011559.0%
2020-03-31200127440200127440→2001273850.1926+0.00023640.9%
2020-04-30200128873362820→2685620.1146+0.00061328.3%
2020-04-30200095646200095646→2000021640.0913+0.00001622.9%
2020-04-30200102190200102190→4190910.1164+0.00030329.5%
2020-05-31200130500200130500→3660070.6486+0.000059100.0%
2020-05-31200127728200067682→2000007020.3018+0.00000535.7%
2020-05-31200129716200129716→2001276080.8960+0.00012889.5%
2020-05-31200129811200129811→3654560.0341+0.0000127.1%
2020-05-31200001285200001285→2000857430.2972+0.00005286.7%
2020-05-31200128873200128873→3628200.5083+0.0000741.7%
2020-06-30200127996200127996→2001272620.6904+0.00011976.3%
2020-06-30200127728200127728→2000676820.1891+0.00001033.3%
2020-07-31200130475200127769→2001304310.4531+0.00001890.0%
2020-08-31200127757200096386→2000003070.2641+0.00000685.7%
2020-08-31200130527200130527→2000002120.9885+0.00032998.8%
2020-09-30200130549200130549→2001263720.3483+0.00013965.6%
2020-09-30365376348454→134630.1010+0.0000000.0%
2020-09-30366024365922→4227370.0058+0.0000000.0%
2020-09-30416078416078→4160480.8862+0.00000777.8%
2020-09-30200130528200130528→2496900.1937+0.00002131.8%
2020-09-30200127360200127360→2000143680.5380+0.00035653.8%
2020-10-31200130697200130697→3665060.7994+0.00199099.9%
2020-10-31200127478200127478→2000397650.2244+0.0000069.2%
2020-10-31200128854200130599→2001304940.0105+0.0000000.0%
2020-11-30422554422554→3654290.1349+0.00017632.7%
2020-11-30200130719200130719→2000014130.4541+0.00022191.0%
2020-11-30200102197200127555→621260.0841+0.00000520.0%
2020-11-30200130688200130688→4171460.8013+0.00040980.0%
2020-11-30200127743200127385→2925940.0365+0.00011112.0%
2020-11-30200130684404921→4057720.9440+0.000004100.0%
2020-12-31200130690200130690→2001276380.7655+0.00014991.4%
2020-12-31200130507200130542→2001305030.0828+0.00001627.1%
2020-12-31200130842200130842→2001304261.0000+0.000129100.0%
2020-12-31200130665200130665→2001305980.6485+0.00108696.0%
2020-12-31200127343365990→2000019420.3135+0.0000000.0%
2020-12-31200130585200130585→2001278130.2576+0.00038846.7%
2020-12-31200130583200130583→2001274790.4699+0.00004266.7%
2021-01-31200127272200127813→3653480.3428+0.00006423.4%
2021-01-31200130903200130903→3649770.3448+0.000177100.0%
2021-01-3136602462126→3659220.0928+0.0000000.0%
2021-01-31200130855200130855→4226000.4995+0.00104899.9%
2021-01-31200130892365922→2001275310.1319+0.00043942.2%
2021-01-31Private ClientPrivate Client→3664190.0240+0.0000000.0%
2021-02-28200127764200127764→2001016140.1127+0.0000302.1%
2021-02-28200130892200130892→3659220.5500+0.00064744.9%
2021-02-28307388200127531→2000710750.0570+0.0000000.0%
2021-03-31200127350200127350→2001273350.1628+0.00000337.5%
2021-03-31200127367200127367→2000004070.2393+0.00007772.0%
2021-03-31200130977200130977→2000628820.1316+0.00015239.6%
2021-03-31200130684405772→4049210.8336+0.00000250.0%
2021-04-30200127268200127268→2001272720.3098+0.00010141.4%
2021-04-30200131052200131052→2001307880.8768+0.00030787.5%
2021-04-30365368365368→3664220.2554+0.00005879.5%
2021-04-30200131056200131056→4116740.2280+0.00001443.8%
2021-05-31200127272200127272→2001278130.1389+0.00011116.7%
2021-05-31200131082200131082→2001309270.1194+0.00004712.0%
2021-05-31419789419789→2001308830.6666+0.000009100.0%
2021-05-31365127365127→2001308050.0442+0.00013211.7%
2021-05-31200131081200131081→2001304190.0072+0.0000061.6%
2021-06-30200127343200127343→3659900.1499+0.0000000.0%
2021-06-30200130684366005→4057720.0317+0.0000012.1%
2021-06-3036537613463→3484540.1185+0.0000000.0%
2021-06-30200131386200131386→2001273450.1409+0.00005922.1%
2021-07-31365376365376→134630.2041+0.0000000.0%
2021-07-31366602366602→2000021620.3394+0.00024082.5%
2021-08-31366559366559→620130.0263+0.0000676.9%
2021-08-31200130507200130507→2001305420.7661+0.00005789.1%
2021-08-31200129815200129815→2001273080.1151+0.00000637.5%
2021-08-31200126526200126526→2001266470.9772+0.00027997.5%
2021-08-31366470366470→640430.3384+0.00337755.0%
2021-08-31200131303200127496→3664460.0455+0.00006714.9%
2021-08-31200102151200102151→3649180.9273+0.0000000.0%
2021-08-31365328365328→3655120.0907+0.00001825.7%
2021-08-31200055274200127325→2001274930.2314+0.00001037.0%
2021-08-31365807365807→2000104480.0868+0.00008126.9%
2021-08-31364929364929→3646970.0104+0.0004433.4%
2021-08-31418961418961→2001019180.0081+0.0002692.6%
2021-08-31200128854200128854→2001305990.0119+0.0000063.2%
2021-08-31365377365377→3665400.0903+0.00073424.1%
2021-08-31420350420350→670030.0228+0.0011814.7%
2021-08-31365266365953→2000856160.2355+0.0000000.0%
2021-08-31200096331348454→3659530.0027+0.0000000.0%
2021-08-31200131301200131301→3657701.0000+0.000186100.0%
2021-08-31200127814214583→2000017470.0110+0.0000082.4%
2021-08-31200130841200130841→2001308190.2392+0.00000250.0%
2021-08-31365236200127644→670040.0167+0.0000054.0%
2021-08-31366131366131→2001274620.3037+0.00002976.3%
2021-08-31422576422576→4188970.0545+0.00012017.2%
2021-08-31364999364999→2001278990.0216+0.0002177.1%
2021-08-31200081805200081805→2000805730.9740+0.00024497.2%
2021-08-31200059378200059378→3647400.0235+0.0000000.0%
2021-08-31366386366386→3663920.9276+0.00001292.3%
2021-08-31365241365241→3651370.9965+0.000226100.0%
2021-08-31200096001200096001→4184830.0522+0.00006912.6%
2021-08-31365242365242→2001309200.1120+0.00068435.9%
2021-08-31200001942200001942→2000358300.0702+0.00011521.9%
2021-08-31200131294200008561→4170720.3080+0.00027692.6%
2021-09-30200131309200131309→2000782890.1634+0.00048343.6%
2021-09-30200127814200127531→2145830.2437+0.00032641.6%
2021-09-3030738864043→2001275310.2385+0.0000000.0%
2021-09-30200127757200127757→2000963860.0217+0.0000075.6%
2021-09-30200131343200131343→2001309900.8852+0.0000000.0%
2021-09-30200131340200131340→3431210.5126+0.00030452.1%
2021-09-3036530462013→3652360.8954+0.0000000.0%
2021-09-30200131303200131303→2001274960.9844+0.00044597.2%
2021-09-30200131325200131325→3529580.8901+0.00087798.9%
2021-10-31365304365236→620130.8381+0.0000000.0%
2021-10-31200131387200131387→2001263450.1625+0.00001248.0%
2021-11-30200131294420239→2000085610.3226+0.00015048.7%
2021-11-3036530462013→3652360.8031+0.0000000.0%
2021-11-30200131449200131449→4190900.6422+0.00017092.9%
2021-11-30200130684200130684→3660050.2861+0.00004032.0%
2021-12-31200137353200131521→2000860360.1795+0.00004335.8%
2021-12-3130738867004→640430.0979+0.0000000.0%
2021-12-31200131534200131534→2001273230.7073+0.00012277.2%
2021-12-31365304365236→620130.7733+0.0000000.0%
2022-01-31200131580200131580→2000710760.9941+0.00320599.4%
2022-01-31200131579200131579→2001271780.4803+0.00016696.5%
2022-01-31200131543200131543→2001019710.0011+0.0000010.1%
2022-01-3136530462013→3652360.7438+0.0000000.0%
2022-02-28200127528364740→2000548160.1777+0.0000000.0%
2022-02-28365304365236→620130.7871+0.0000000.0%
2022-03-3136602464043→621260.2343+0.0000000.0%
2022-03-31200131651200124661→2001262610.9184+0.00002496.0%
2022-03-31200131648200131648→2001309500.5920+0.00012559.2%
2022-04-30200131722200010610→3653940.0141+0.0000482.5%
2022-04-30200131750200131750→3652160.6573+0.00215165.7%
2022-04-3036530462013→3652360.7640+0.0000000.0%
2022-04-30200131294200131294→4202390.1730+0.0001149.6%
2022-04-30200126380200126380→2001018940.6341+0.00129850.7%
2022-05-3136602462126→640430.2692+0.0000000.0%
2022-05-31200131777200131777→4223450.9973+0.00043299.8%
2022-05-31200131771200131771→2001271270.0755+0.00005015.2%
2022-05-31200131763200131763→2001277060.9153+0.00084891.5%
2022-05-3136530467003→620130.3815+0.0000000.0%
2022-06-30200127175200127175→2000156990.1282+0.00000423.5%
2022-06-30200131722200131722→2000106100.3899+0.00192749.8%
2022-06-30200127516200127516→2001366370.0398+0.0000010.2%
2022-06-30200127201200127628→2001019610.1361+0.00000327.3%
2022-06-30200131804200131804→3654900.0919+0.00010427.5%
2022-07-31365236365236→2001276440.1597+0.0000235.9%
2022-07-31200131651200126261→2001246610.6659+0.00003368.8%
2022-07-3136602464043→621260.2343+0.0000000.0%
2022-07-31200127495200127495→2001290850.3333+0.000001100.0%
2022-08-31200136597200136597→2001307910.4507+0.00040490.0%
2022-08-31200127528200054816→3647400.0245+0.0000000.0%
2022-08-31200127479200127814→3660240.0571+0.00000412.9%
2022-08-31200131651200131651→2001262610.4859+0.00002233.3%
2022-09-30200137356366024→2001273750.1991+0.00008439.8%
2022-09-30200137354200137354→2000096890.0220+0.0000204.5%
2022-09-30200137477200085616→3658360.4469+0.00003362.3%
2022-09-30200127479200127375→2001278140.0937+0.00002923.0%
2022-09-30200137360200137360→640030.1202+0.00084528.2%
2022-09-30200102197200102197→2001275550.1988+0.0000044.6%
2022-09-30200131736200131736→2001308790.4435+0.00002172.4%
2022-09-3036530462013→670030.3493+0.0000000.0%
2022-09-30200137361200137361→2011130.6549+0.00024967.1%
2022-10-31200137477200137477→2000856160.0993+0.00003219.3%
2022-10-3120013762864003→4189920.3844+0.00086950.5%
2022-10-31200002583200002583→2000331040.1897+0.00000520.0%
2022-10-3120013735667003→3660240.1374+0.00008014.3%
2022-10-31200137633200137633→2001298970.1259+0.00000125.0%
2022-10-31200137467200127262→2001272560.3748+0.00007764.7%
2022-10-31200127872200127872→2001317800.0603+0.0000018.3%
2022-11-30200137459200127813→3656470.0719+0.00003923.2%
2022-11-30200127528200127905→2000548160.1211+0.0000000.0%
2022-11-30200127127200127156→2001272550.1045+0.00000523.8%
2022-11-30200127627200127627→2001313450.3162+0.00012526.5%
2022-12-31200137459200137459→2001278130.1151+0.00014325.5%
2022-12-31200137353200137353→2001315210.7696+0.00012672.0%
2022-12-31200002076200002076→2000019410.0157+0.0000724.7%
2022-12-31200137619200137619→2001272640.1563+0.00014632.7%
2022-12-31200137467200137467→2001272620.2359+0.00002910.7%
2022-12-31200127479200127479→2001273750.5167+0.00009655.8%
2022-12-31200131477200131477→2001273900.2801+0.00028084.1%
2022-12-31200137356200137356→670030.1234+0.00041521.7%
2023-01-3136602462126→640430.2692+0.0000015.6%
2023-01-31200137759418897→4193360.1734+0.00006541.4%
2023-01-31200058108200058108→3124780.8098+0.00769482.4%
2023-01-31200127528364559→2001279050.3088+0.0000000.0%
2023-02-2836530467003→620130.3815+0.0000000.0%
2023-02-28200127813200127555→2001281740.2096+0.00008752.4%
2023-03-31200127528200065564→3645590.3023+0.0000000.0%
2023-03-3120012764464043→620870.3063+0.0000000.0%
2023-03-31307388418992→670040.2582+0.0000000.0%
2023-03-31200137828200137828→2000002520.8399+0.00133099.8%
2023-03-31200127813200128174→2001275550.1733+0.00004511.8%
2023-04-30200137851200137851→3655000.7135+0.00012596.9%
2023-04-3036530462013→670030.3493+0.0000000.0%
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Generated by carmignac_analysis.py · Carmignac × ENSAE Data Challenge 2025
+ + + + + \ No newline at end of file