Prepare source-only public release for develop.

Add cluster audit pipeline, united EA updates, brochure generators, and publication hygiene (gitignore, MT5 path desensitization, pre-upload scan). Remove tracked reports, models, and binary artifacts from the repo. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-02 15:03:43 +02:00
parent 3f75a08848
commit 605faf5310
1014 changed files with 83437 additions and 10413 deletions
@@ -0,0 +1,91 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+import pandas as pd
+
+from .expressions import compile_expression
+
+
+@dataclass
+class EngineConfig:
+    factor_expression: str
+    rebalance_frequency: str = "M"  # D/W/M/Q
+    long_quantile: float = 0.2
+    short_quantile: float = 0.2
+    long_short: bool = True
+
+
+def run_factor_engine(df: pd.DataFrame, cfg: EngineConfig) -> tuple[pd.DataFrame, pd.DataFrame]:
+    data = df.copy()
+    data = data.sort_values(["date", "asset"]).reset_index(drop=True)
+    data["ret_1d"] = data.groupby("asset")["close"].pct_change().fillna(0.0)
+
+    expr = compile_expression(cfg.factor_expression)
+    data["score"] = expr.eval(data).replace([np.inf, -np.inf], np.nan)
+
+    rebalance_key = data["date"].dt.to_period(cfg.rebalance_frequency).astype(str)
+    data["rebalance_key"] = rebalance_key
+
+    weights = []
+    for _, bucket in data.groupby("rebalance_key"):
+        last_day = bucket["date"].max()
+        snap = bucket[bucket["date"] == last_day].copy()
+        snap = snap.dropna(subset=["score"])
+        if snap.empty:
+            continue
+
+        q_long = snap["score"].quantile(1.0 - cfg.long_quantile)
+        longs = snap[snap["score"] >= q_long][["asset"]].copy()
+        longs["w"] = 1.0 / max(len(longs), 1)
+
+        if cfg.long_short and cfg.short_quantile > 0:
+            q_short = snap["score"].quantile(cfg.short_quantile)
+            shorts = snap[snap["score"] <= q_short][["asset"]].copy()
+            shorts["w"] = -1.0 / max(len(shorts), 1)
+            snap_w = pd.concat([longs, shorts], ignore_index=True)
+        else:
+            snap_w = longs
+
+        snap_w["effective_date"] = last_day
+        weights.append(snap_w)
+
+    if not weights:
+        empty = pd.DataFrame(columns=["date", "portfolio_ret", "equity"])
+        return empty, data
+
+    wdf = pd.concat(weights, ignore_index=True)
+    data = data.merge(wdf, how="left", left_on=["date", "asset"], right_on=["effective_date", "asset"])
+    data["w"] = data.groupby("asset")["w"].ffill().fillna(0.0)
+    data["contrib"] = data["w"] * data["ret_1d"]
+
+    daily = data.groupby("date", as_index=False)["contrib"].sum().rename(columns={"contrib": "portfolio_ret"})
+    daily["equity"] = (1.0 + daily["portfolio_ret"]).cumprod()
+    return daily, data
+
+
+def summary_metrics(equity_curve: pd.DataFrame) -> dict:
+    if equity_curve.empty:
+        return {"cagr": 0.0, "sharpe": 0.0, "max_dd": 0.0, "total_return": 0.0}
+
+    rets = equity_curve["portfolio_ret"]
+    total_return = equity_curve["equity"].iloc[-1] - 1.0
+
+    n = len(rets)
+    ann = 252
+    cagr = (equity_curve["equity"].iloc[-1] ** (ann / max(n, 1))) - 1.0
+    vol = rets.std(ddof=0) * np.sqrt(ann)
+    sharpe = (rets.mean() * ann) / vol if vol > 1e-12 else 0.0
+
+    rolling_max = equity_curve["equity"].cummax()
+    dd = equity_curve["equity"] / rolling_max - 1.0
+    max_dd = dd.min()
+
+    return {
+        "cagr": float(cagr),
+        "sharpe": float(sharpe),
+        "max_dd": float(max_dd),
+        "total_return": float(total_return),
+    }
+