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aggiunti salvataggi plot e contatore avanzamento

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This commit is contained in:
fredmaloggia
2025-12-30 15:07:30 +01:00
parent 5f668cbb6d
commit e8f57afa99
3 changed files with 405 additions and 4 deletions
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149
Trading Pattern Recon w Hurst - Forex.py
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@@ -43,6 +43,7 @@ import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import requests
import time
# -------------------------
# Wavelets
@@ -150,7 +151,6 @@ DENOISE_WAVELET = "db4" # DB family (Daubechies)
DENOISE_LEVEL = 3
DENOISE_MIN_LEN = 96
DENOISE_THRESHOLD_MODE = "soft"
DAYS_PER_YEAR = 252
OUT_DIR = Path("./out_forex")
@@ -518,6 +518,125 @@ def plot_heatmap_monthly(r: pd.Series, title: str):
return fig
# ------------------------------------------------------------
# Progress timer (post-test checkpoints)
# ------------------------------------------------------------
def _format_eta(seconds):
if seconds is None or seconds != seconds:
return "n/a"
seconds = max(0, int(round(seconds)))
minutes, secs = divmod(seconds, 60)
hours, minutes = divmod(minutes, 60)
if hours:
return f"{hours}h {minutes:02d}m {secs:02d}s"
return f"{minutes}m {secs:02d}s"
_post_timer = {"t0": None, "tprev": None, "total": None, "done": 0}
def start_post_timer(total_steps: int):
_post_timer["t0"] = time.perf_counter()
_post_timer["tprev"] = _post_timer["t0"]
_post_timer["total"] = total_steps
_post_timer["done"] = 0
def checkpoint_post_timer(label: str):
if _post_timer["t0"] is None or _post_timer["total"] is None:
return
_post_timer["done"] += 1
now = time.perf_counter()
step_dt = now - _post_timer["tprev"]
total_dt = now - _post_timer["t0"]
avg = total_dt / max(_post_timer["done"], 1)
eta = avg * max(_post_timer["total"] - _post_timer["done"], 0)
print(f"[TIMER] post {_post_timer['done']}/{_post_timer['total']} {label} - step {step_dt:.2f}s, total {total_dt:.2f}s, ETA {_format_eta(eta)}")
_post_timer["tprev"] = now
def _currency_allocation_from_exposure(exp_df: pd.DataFrame) -> pd.DataFrame:
"""Convert net currency exposure to normalized gross allocation by currency."""
if exp_df is None or getattr(exp_df, "empty", True):
return pd.DataFrame()
W = exp_df.copy().apply(pd.to_numeric, errors="coerce").fillna(0.0)
if W.index.has_duplicates:
W = W[~W.index.duplicated(keep="last")]
W = W.sort_index()
W = W.abs()
keep_cols = [c for c in W.columns if float(np.abs(W[c]).sum()) > 0.0]
if keep_cols:
W = W[keep_cols]
row_sum = W.sum(axis=1).replace(0, np.nan)
W = W.div(row_sum, axis=0).fillna(0.0)
return W
def plot_portfolio_composition_fixed(
weights: pd.DataFrame,
title: str,
save_path: Path | None = None,
max_legend: int = 20,
):
"""Stacked area dei pesi nel tempo (allocazione per valuta)."""
if weights is None or getattr(weights, "empty", True):
print(f"[SKIP] Nessun peso per: {title}")
return
W = weights.copy().apply(pd.to_numeric, errors="coerce").fillna(0.0)
if W.index.has_duplicates:
W = W[~W.index.duplicated(keep="last")]
W = W.sort_index()
keep_cols = [c for c in W.columns if float(np.abs(W[c]).sum()) > 0.0]
if not keep_cols:
print(f"[SKIP] Tutti i pesi sono zero per: {title}")
return
W = W[keep_cols]
if len(W.index) < 2:
print(f"[SKIP] Serie troppo corta per: {title}")
return
avg_w = W.mean(0).sort_values(ascending=False)
ordered = avg_w.index.tolist()
if len(ordered) > max_legend:
head = ordered[:max_legend]
tail = [c for c in ordered if c not in head]
W_show = W[head].copy()
if tail:
W_show["Other"] = W[tail].sum(1)
ordered = head + ["Other"]
else:
ordered = head
else:
W_show = W[ordered].copy()
cmap = plt.colormaps.get_cmap("tab20")
colors = [cmap(i % cmap.N) for i in range(len(ordered))]
fig, ax = plt.subplots(figsize=(11, 6))
ax.stackplot(W_show.index, [W_show[c].values for c in ordered], labels=ordered, colors=colors)
ax.set_title(f"Composizione valute nel tempo - {title}")
ymax = float(np.nanmax(W_show.sum(1).values))
if not np.isfinite(ymax) or ymax <= 0:
ymax = 1.0
ax.set_ylim(0, max(1.0, ymax))
ax.grid(True, alpha=0.3)
ax.set_ylabel("Peso")
ax.set_yticks(ax.get_yticks())
ax.set_yticklabels([f"{y*100:.0f}%" for y in ax.get_yticks()])
ncol = 2 if len(ordered) > 10 else 1
ax.legend(loc="upper left", bbox_to_anchor=(1.01, 1), frameon=False, ncol=ncol, title="Currency")
fig.tight_layout()
if save_path:
fig.savefig(save_path, dpi=150, bbox_inches="tight")
print(f"[INFO] Salvato: {save_path}")
plt.close(fig)
def inverse_vol_weights(returns_df: pd.DataFrame, window: int, max_weight: float | None) -> pd.DataFrame:
vol = returns_df.rolling(window).std()
inv = 1 / vol.replace(0, np.nan)
@@ -686,6 +805,7 @@ def main():
print(f"Fee: {FEE_BPS} bp | Short: {ALLOW_SHORT} | Currency cap: {CURRENCY_CAP:.2f}")
print(f"Wavelet denoise: {DENOISE_ENABLED} ({DENOISE_WAVELET}, level={DENOISE_LEVEL}, min_len={DENOISE_MIN_LEN})")
print("Esecuzione: close(t), PnL: close(t+1)/close(t)\n")
start_post_timer(5)
# 1) Fetch prices
prices: dict[str, pd.DataFrame] = {}
@@ -699,12 +819,16 @@ def main():
if len(prices) < 5:
raise RuntimeError(f"Pochi ticker validi ({len(prices)}).")
checkpoint_post_timer("Price fetch")
# 2) Per ticker backtest
hurst_rows = []
summary_rows = []
sig_rows = []
for tkr, dfp in prices.items():
total = len(prices)
for i, (tkr, dfp) in enumerate(prices.items(), 1):
print(f"[{i}/{total}] Testing {tkr} ...")
if "AdjClose" not in dfp.columns:
continue
@@ -757,6 +881,8 @@ def main():
if not sig_rows:
raise RuntimeError("Nessun ticker backtestato con successo")
checkpoint_post_timer("Per-ticker backtest")
hurst_df = pd.DataFrame(hurst_rows).sort_values("Ticker").reset_index(drop=True)
summary_df = pd.DataFrame(summary_rows).sort_values("Ticker").reset_index(drop=True)
signals_df = pd.concat(sig_rows, ignore_index=True)
@@ -810,6 +936,8 @@ def main():
eq_eq = equity_from_returns(ret_eq).rename("Eq_EqW_TopN")
eq_rp = equity_from_returns(ret_rp).rename("Eq_RP_TopN")
checkpoint_post_timer("Portfolio build")
# 6) Plots
plt.figure(figsize=(10, 5))
plt.plot(eq_eq, label=f"Equal Weight (Top{TOP_N}, fee {FEE_BPS}bp)")
@@ -829,6 +957,22 @@ def main():
fig_rp.savefig(PLOT_DIR / "heatmap_rp.png", dpi=150)
plt.close(fig_rp)
ccy_eq_alloc = _currency_allocation_from_exposure(ccy_eq)
ccy_rp_alloc = _currency_allocation_from_exposure(ccy_rp)
plot_portfolio_composition_fixed(
ccy_eq_alloc,
"Equal Weight (currency gross)",
PLOT_DIR / "composition_equal_weight_active.png",
)
plot_portfolio_composition_fixed(
ccy_rp_alloc,
"Risk Parity (currency gross)",
PLOT_DIR / "composition_risk_parity_active.png",
)
checkpoint_post_timer("Plots")
# 7) Export
hurst_df.to_csv(OUT_DIR / "hurst.csv", index=False)
summary_df.to_csv(OUT_DIR / "forward_bt_summary.csv", index=False)
@@ -845,6 +989,7 @@ def main():
ccy_eq.to_csv(OUT_DIR / "currency_exposure_eq.csv")
ccy_rp.to_csv(OUT_DIR / "currency_exposure_rp.csv")
checkpoint_post_timer("Exports")
print(f"\nSaved to: {OUT_DIR.resolve()}\n")