230 lines
8.5 KiB
Python
230 lines
8.5 KiB
Python
|
from __future__ import annotations
|
||
|
|
|
||
|
|
import argparse
|
||
|
|
import itertools
|
||
|
|
import json
|
||
|
|
from dataclasses import replace
|
||
|
|
from pathlib import Path
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pandas as pd
|
||
|
|
|
||
|
|
from qfr.strategy.etf_trend import Constraints, TrendParams, UniverseAsset, compute_features, portfolio_vol, risk_parity_weights, select_portfolio
|
||
|
|
|
||
|
|
|
||
|
|
def load_universe(config_path: Path) -> tuple[list[UniverseAsset], Constraints, str, str]:
|
||
|
|
conf = json.loads(config_path.read_text(encoding="utf-8"))
|
||
|
|
universe = [UniverseAsset(**a) for a in conf["assets"]]
|
||
|
|
|
||
|
|
cons = conf.get("constraints", {})
|
||
|
|
constraints = Constraints(
|
||
|
|
max_positions=int(cons.get("max_positions", 4)),
|
||
|
|
must_commodity=int(cons.get("must_include", {}).get("commodity", 0)),
|
||
|
|
must_rates=int(cons.get("must_include", {}).get("rates", 0)),
|
||
|
|
must_equity=int(cons.get("must_include", {}).get("equity", 0)),
|
||
|
|
)
|
||
|
|
|
||
|
|
risk_proxy = cons.get("risk_proxy", "510300.SH")
|
||
|
|
rates_fallback = cons.get("rates_fallback", "511010.SH")
|
||
|
|
return universe, constraints, risk_proxy, rates_fallback
|
||
|
|
|
||
|
|
|
||
|
|
def load_prices(raw_dir: Path, universe: list[UniverseAsset], start: str, end: str) -> dict[str, pd.DataFrame]:
|
||
|
|
out: dict[str, pd.DataFrame] = {}
|
||
|
|
for a in universe:
|
||
|
|
fn = raw_dir / f"{a.ts_code.replace('.', '')}.parquet"
|
||
|
|
df = pd.read_parquet(fn)
|
||
|
|
df = df.copy()
|
||
|
|
df["trade_date"] = df["trade_date"].astype(str)
|
||
|
|
df = df[(df["trade_date"] >= start) & (df["trade_date"] <= end)]
|
||
|
|
out[a.ts_code] = df
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def perf_stats(equity: pd.Series) -> dict[str, float]:
|
||
|
|
r = equity.pct_change().dropna()
|
||
|
|
if r.empty:
|
||
|
|
return {}
|
||
|
|
ann_ret = float((equity.iloc[-1] / equity.iloc[0]) ** (252 / len(r)) - 1)
|
||
|
|
ann_vol = float(r.std(ddof=1) * (252 ** 0.5))
|
||
|
|
dd = float((equity / equity.cummax() - 1.0).min())
|
||
|
|
calmar = float(ann_ret / abs(dd)) if dd < 0 else float("nan")
|
||
|
|
return {"ann_return": ann_ret, "ann_vol": ann_vol, "max_drawdown": dd, "calmar": calmar}
|
||
|
|
|
||
|
|
|
||
|
|
def run_backtest_cached(
|
||
|
|
feats: dict[str, pd.DataFrame],
|
||
|
|
universe: list[UniverseAsset],
|
||
|
|
constraints: Constraints,
|
||
|
|
params: TrendParams,
|
||
|
|
rates_fallback: str,
|
||
|
|
risk_proxy: str,
|
||
|
|
) -> pd.DataFrame:
|
||
|
|
# align dates intersection
|
||
|
|
dates = None
|
||
|
|
for f in feats.values():
|
||
|
|
d = set(f["trade_date"].astype(str))
|
||
|
|
dates = d if dates is None else dates.intersection(d)
|
||
|
|
if not dates:
|
||
|
|
raise RuntimeError("No overlapping trade_date")
|
||
|
|
all_dates = sorted(dates)
|
||
|
|
|
||
|
|
close_px = pd.DataFrame(index=all_dates)
|
||
|
|
ret1 = pd.DataFrame(index=all_dates)
|
||
|
|
for ts, f in feats.items():
|
||
|
|
g = f.set_index("trade_date").reindex(all_dates)
|
||
|
|
close_px[ts] = g["close"].astype(float)
|
||
|
|
ret1[ts] = close_px[ts].pct_change().fillna(0.0)
|
||
|
|
|
||
|
|
if risk_proxy not in close_px.columns:
|
||
|
|
raise RuntimeError("risk_proxy missing")
|
||
|
|
|
||
|
|
weights = pd.DataFrame(0.0, index=all_dates, columns=close_px.columns)
|
||
|
|
|
||
|
|
in_pos: set[str] = set()
|
||
|
|
highest_close: dict[str, float] = {}
|
||
|
|
|
||
|
|
atr_map = {ts: feats[ts].set_index("trade_date").reindex(all_dates)["atr"].astype(float) for ts in close_px.columns}
|
||
|
|
mf_map = {ts: feats[ts].set_index("trade_date").reindex(all_dates)["ma_fast"].astype(float) for ts in close_px.columns}
|
||
|
|
ms_map = {ts: feats[ts].set_index("trade_date").reindex(all_dates)["ma_slow"].astype(float) for ts in close_px.columns}
|
||
|
|
|
||
|
|
last_reb = -10**9
|
||
|
|
|
||
|
|
for i, d in enumerate(all_dates):
|
||
|
|
if i > 0:
|
||
|
|
weights.loc[d] = weights.iloc[i - 1]
|
||
|
|
|
||
|
|
for ts in list(in_pos):
|
||
|
|
c = float(close_px.loc[d, ts])
|
||
|
|
if np.isfinite(c):
|
||
|
|
highest_close[ts] = max(highest_close.get(ts, c), c)
|
||
|
|
|
||
|
|
# exits
|
||
|
|
for ts in list(in_pos):
|
||
|
|
c = float(close_px.loc[d, ts])
|
||
|
|
mf = float(mf_map[ts].loc[d])
|
||
|
|
ms = float(ms_map[ts].loc[d])
|
||
|
|
atr = float(atr_map[ts].loc[d])
|
||
|
|
h = highest_close.get(ts, c)
|
||
|
|
trend_break = (np.isfinite(mf) and np.isfinite(ms) and (mf < ms))
|
||
|
|
chand_break = np.isfinite(atr) and c < (h - params.atr_mult * atr)
|
||
|
|
if trend_break or chand_break:
|
||
|
|
weights.loc[d, ts] = 0.0
|
||
|
|
in_pos.remove(ts)
|
||
|
|
highest_close.pop(ts, None)
|
||
|
|
|
||
|
|
if (i - last_reb) >= params.rebalance_every:
|
||
|
|
rows = []
|
||
|
|
for ts in close_px.columns:
|
||
|
|
f = feats[ts].set_index("trade_date").reindex([d]).iloc[0]
|
||
|
|
rows.append((ts, bool(f["trend_ok"]) if pd.notna(f["trend_ok"]) else False,
|
||
|
|
float(f["score_raw"]) if pd.notna(f["score_raw"]) else float("nan"),
|
||
|
|
float(f["vol"]) if pd.notna(f["vol"]) else float("nan")))
|
||
|
|
snap = pd.DataFrame(rows, columns=["ts_code", "trend_ok", "score_raw", "vol"]).set_index("ts_code")
|
||
|
|
|
||
|
|
picks = select_portfolio(snap, universe, constraints)
|
||
|
|
vol = snap.loc[picks, "vol"].copy()
|
||
|
|
w = risk_parity_weights(vol, max_w=0.50)
|
||
|
|
|
||
|
|
trailing = ret1[picks].iloc[max(0, i - params.port_vol_window + 1) : i + 1]
|
||
|
|
pvol = portfolio_vol(trailing, w)
|
||
|
|
scale = 1.0
|
||
|
|
if np.isfinite(pvol) and pvol > 0:
|
||
|
|
scale = min(1.0, params.target_ann_vol / pvol)
|
||
|
|
|
||
|
|
w_exec = w * scale
|
||
|
|
weights.loc[d] = 0.0
|
||
|
|
for ts, wi in w_exec.items():
|
||
|
|
weights.loc[d, ts] = float(wi)
|
||
|
|
|
||
|
|
rem = 1.0 - float(w_exec.sum())
|
||
|
|
if rem > 1e-12 and rates_fallback in weights.columns:
|
||
|
|
weights.loc[d, rates_fallback] += rem
|
||
|
|
|
||
|
|
in_pos = {ts for ts in close_px.columns if weights.loc[d, ts] > 1e-12}
|
||
|
|
for ts in in_pos:
|
||
|
|
c = float(close_px.loc[d, ts])
|
||
|
|
highest_close[ts] = max(highest_close.get(ts, c), c)
|
||
|
|
|
||
|
|
last_reb = i
|
||
|
|
|
||
|
|
w_lag = weights.shift(1).fillna(0.0)
|
||
|
|
port_ret = (ret1 * w_lag).sum(axis=1)
|
||
|
|
equity = (1.0 + port_ret).cumprod().to_frame("equity")
|
||
|
|
return equity
|
||
|
|
|
||
|
|
|
||
|
|
def main() -> None:
|
||
|
|
ap = argparse.ArgumentParser()
|
||
|
|
ap.add_argument("--config", default="configs/etf_universe.json")
|
||
|
|
ap.add_argument("--rawdir", default="data/raw")
|
||
|
|
ap.add_argument("--start", default="20200101")
|
||
|
|
ap.add_argument("--end", default="20251231")
|
||
|
|
ap.add_argument("--out", default="data/tune_results_fast.parquet")
|
||
|
|
args = ap.parse_args()
|
||
|
|
|
||
|
|
universe, constraints, risk_proxy, rates_fallback = load_universe(Path(args.config))
|
||
|
|
prices = load_prices(Path(args.rawdir), universe, args.start, args.end)
|
||
|
|
|
||
|
|
base = TrendParams(rebalance_every=1)
|
||
|
|
|
||
|
|
# grid (keep small)
|
||
|
|
fast_list = [3, 5, 8]
|
||
|
|
slow_list = [15, 20, 30]
|
||
|
|
atr_mult_list = [2.0, 2.5, 3.0]
|
||
|
|
vol_window_list = [10, 20]
|
||
|
|
port_vol_window_list = [40, 60]
|
||
|
|
max_positions_list = [3, 4]
|
||
|
|
|
||
|
|
rows = []
|
||
|
|
|
||
|
|
for sma_fast, sma_slow in itertools.product(fast_list, slow_list):
|
||
|
|
if sma_fast >= sma_slow:
|
||
|
|
continue
|
||
|
|
for atr_mult, vol_window, port_vol_window, max_positions in itertools.product(
|
||
|
|
atr_mult_list, vol_window_list, port_vol_window_list, max_positions_list
|
||
|
|
):
|
||
|
|
params = replace(
|
||
|
|
base,
|
||
|
|
max_positions=max_positions,
|
||
|
|
sma_fast=sma_fast,
|
||
|
|
sma_slow=sma_slow,
|
||
|
|
atr_mult=atr_mult,
|
||
|
|
vol_window=vol_window,
|
||
|
|
port_vol_window=port_vol_window,
|
||
|
|
)
|
||
|
|
cons = replace(constraints, max_positions=max_positions)
|
||
|
|
|
||
|
|
feats = {ts: compute_features(df, params) for ts, df in prices.items()}
|
||
|
|
equity = run_backtest_cached(feats, universe, cons, params, rates_fallback, risk_proxy)
|
||
|
|
st = perf_stats(equity["equity"])
|
||
|
|
if not st:
|
||
|
|
continue
|
||
|
|
rows.append({
|
||
|
|
"sma_fast": sma_fast,
|
||
|
|
"sma_slow": sma_slow,
|
||
|
|
"atr_mult": atr_mult,
|
||
|
|
"vol_window": vol_window,
|
||
|
|
"port_vol_window": port_vol_window,
|
||
|
|
"max_positions": max_positions,
|
||
|
|
**st,
|
||
|
|
})
|
||
|
|
|
||
|
|
df = pd.DataFrame(rows)
|
||
|
|
if df.empty:
|
||
|
|
print("no results")
|
||
|
|
return
|
||
|
|
|
||
|
|
filt = df[df["ann_vol"] <= 0.18].sort_values(["ann_return", "calmar"], ascending=False)
|
||
|
|
out = Path(args.out)
|
||
|
|
out.parent.mkdir(parents=True, exist_ok=True)
|
||
|
|
filt.to_parquet(out, index=False)
|
||
|
|
|
||
|
|
cols = ["ann_return", "ann_vol", "max_drawdown", "calmar", "sma_fast", "sma_slow", "atr_mult", "vol_window", "port_vol_window", "max_positions"]
|
||
|
|
print("top10")
|
||
|
|
print(filt[cols].head(10).to_string(index=False))
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|