refactor: riorganizzazione script — Strategy ABC, folder strategies/waste/analysis
- src/strategies/base.py: Strategy ABC con Signal, BacktestResult, YearlyStats - src/strategies/indicators.py: keltner_ratio, detect_squeezes, ema, atr, rv, corr - scripts/strategies/: SQ01-SQ04 (squeeze puro/filtri), ML01 (squeeze+GBM) - scripts/waste/: W01-W22 script scartati + REF originali - scripts/analysis/: compare, best_yearly, final_report, paper_status - CLAUDE.md aggiornato con nuova struttura e tabella strategie Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -0,0 +1,204 @@
|
||||
"""SQ04 — Ultimate Squeeze — combinazione incrementale di tutti i filtri.
|
||||
|
||||
Testa combinazioni di filtri (antifake, long_sq, timing, cross-asset,
|
||||
correlation, volume, trend alignment, volatility regime) e classifica
|
||||
per accuracy.
|
||||
|
||||
IN:
|
||||
- OHLCV DataFrame (primario + secondario)
|
||||
- Parametri: bb_window, sq_threshold, lista filtri da attivare
|
||||
|
||||
OUT:
|
||||
- BacktestResult per ogni combinazione di filtri
|
||||
- Classifica globale
|
||||
|
||||
Risultati tipici:
|
||||
BTC 15m antifake+corr: 81.6% acc (ma concentrato 2018)
|
||||
BTC 15m antifake+vol: 79.7% acc, 1250 trades — robusto
|
||||
ETH 1h antifake+corr: 80.7% acc (solo 2018)
|
||||
"""
|
||||
from __future__ import annotations
|
||||
import sys
|
||||
sys.path.insert(0, ".")
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from src.strategies.base import Strategy, Signal
|
||||
from src.strategies.indicators import (
|
||||
keltner_ratio, detect_squeezes, ema, rv_annualized, rolling_correlation,
|
||||
)
|
||||
from src.data.downloader import load_data
|
||||
|
||||
|
||||
class SqueezeUltimate(Strategy):
|
||||
name = "SQ04_ultimate"
|
||||
description = "Ultimate squeeze — tutti i filtri combinabili"
|
||||
default_assets = ["BTC", "ETH"]
|
||||
default_timeframes = ["15m", "1h"]
|
||||
|
||||
FILTER_PRESETS = {
|
||||
"antifake+vol": ["antifake", "vol_confirm"],
|
||||
"antifake+corr": ["antifake", "corr_high"],
|
||||
"af+long+corr+trend": ["antifake", "long_sq", "corr_high", "trend_align"],
|
||||
"ALL": ["antifake", "long_sq", "cross", "timing", "corr_high",
|
||||
"vol_confirm", "trend_align", "low_rv"],
|
||||
}
|
||||
|
||||
def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
|
||||
**params) -> list[Signal]:
|
||||
c = df["close"].values
|
||||
h = df["high"].values
|
||||
l = df["low"].values
|
||||
v = df["volume"].values
|
||||
n = len(c)
|
||||
|
||||
asset = params.get("asset", "BTC")
|
||||
tf = params.get("tf", "15m")
|
||||
filters = params.get("filters", ["antifake", "vol_confirm"])
|
||||
|
||||
kcr = keltner_ratio(c, h, l, 14)
|
||||
events = detect_squeezes(c, h, l, kcr)
|
||||
|
||||
secondary = "ETH" if asset == "BTC" else "BTC"
|
||||
df2 = load_data(secondary, tf)
|
||||
c2 = df2["close"].values
|
||||
kcr2 = keltner_ratio(c2, df2["high"].values, df2["low"].values, 14)
|
||||
ts2 = df2["timestamp"].values
|
||||
|
||||
ema_50 = ema(c, 50)
|
||||
rv_48 = rv_annualized(c, 48)
|
||||
corr = rolling_correlation(c, c2)
|
||||
|
||||
signals = []
|
||||
for ev in events:
|
||||
i = ev["idx"]
|
||||
if i < 1 or i >= n:
|
||||
continue
|
||||
first_ret = (c[i] - c[i - 1]) / c[i - 1] if c[i - 1] > 0 else 0
|
||||
if abs(first_ret) < 0.001:
|
||||
continue
|
||||
|
||||
skip = False
|
||||
for f in filters:
|
||||
if f == "antifake":
|
||||
br = h[i] - l[i]
|
||||
if br > 0:
|
||||
if c[i] > c[i-1] and (h[i] - c[i]) / br > 0.6:
|
||||
skip = True
|
||||
elif c[i] <= c[i-1] and (c[i] - l[i]) / br > 0.6:
|
||||
skip = True
|
||||
elif f == "long_sq":
|
||||
if ev["dur"] < 10:
|
||||
skip = True
|
||||
elif f == "timing":
|
||||
if ts.iloc[i].hour < 4 or ts.iloc[i].hour > 16:
|
||||
skip = True
|
||||
elif f == "cross":
|
||||
i2 = np.searchsorted(ts2, ts.values[i].astype("int64") // 10**6)
|
||||
i2 = min(i2, len(kcr2) - 1)
|
||||
if not any(not np.isnan(kcr2[j]) and kcr2[j] < 0.85
|
||||
for j in range(max(0, i2 - 10), i2 + 1)):
|
||||
skip = True
|
||||
elif f == "corr_high":
|
||||
if np.isnan(corr[i]) or abs(corr[i]) < 0.6:
|
||||
skip = True
|
||||
elif f == "vol_confirm":
|
||||
avg_v = np.mean(v[ev["sq_start"]:i])
|
||||
if avg_v > 0 and v[i] <= avg_v * 1.3:
|
||||
skip = True
|
||||
elif f == "trend_align":
|
||||
if not np.isnan(ema_50[i]):
|
||||
if first_ret > 0 and c[i] < ema_50[i]:
|
||||
skip = True
|
||||
elif first_ret < 0 and c[i] > ema_50[i]:
|
||||
skip = True
|
||||
elif f == "low_rv":
|
||||
if not np.isnan(rv_48[i]) and rv_48[i] >= 1.5:
|
||||
skip = True
|
||||
if skip:
|
||||
break
|
||||
|
||||
if skip:
|
||||
continue
|
||||
|
||||
signals.append(Signal(
|
||||
idx=i,
|
||||
direction=1 if first_ret > 0 else -1,
|
||||
entry_price=c[i - 1],
|
||||
metadata={"dur": ev["dur"], "filters": filters},
|
||||
))
|
||||
return signals
|
||||
|
||||
def backtest(self, asset: str, tf: str, hold: int = 3, **params):
|
||||
params.setdefault("asset", asset)
|
||||
params.setdefault("tf", tf)
|
||||
df = load_data(asset, tf)
|
||||
ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
|
||||
signals = self.generate_signals(df, ts, **params)
|
||||
# Usa il backtest della base ma passando i segnali già generati
|
||||
from src.strategies.base import BacktestResult, YearlyStats, TF_MINUTES
|
||||
c = df["close"].values
|
||||
n = len(c)
|
||||
yearly: dict[int, dict] = {}
|
||||
capital = float(self.initial_capital)
|
||||
peak = capital
|
||||
max_dd = 0.0
|
||||
total_bars = 0
|
||||
for sig in signals:
|
||||
i = sig.idx
|
||||
if i + hold >= n or i < 1:
|
||||
continue
|
||||
entry = sig.entry_price
|
||||
exit_price = c[min(i + hold - 1, n - 1)]
|
||||
actual = (exit_price - entry) / entry * sig.direction
|
||||
net = actual * self.leverage - self.fee_rt * self.leverage
|
||||
capital += capital * self.position_size * net
|
||||
capital = max(capital, 10)
|
||||
if capital > peak: peak = capital
|
||||
dd = (peak - capital) / peak
|
||||
max_dd = max(max_dd, dd)
|
||||
total_bars += hold
|
||||
year = ts.iloc[i].year
|
||||
if year not in yearly:
|
||||
yearly[year] = {"w": 0, "t": 0, "pnl": 0.0}
|
||||
yearly[year]["t"] += 1
|
||||
if actual > 0: yearly[year]["w"] += 1
|
||||
yearly[year]["pnl"] += net * self.initial_capital
|
||||
all_t = sum(d["t"] for d in yearly.values())
|
||||
all_w = sum(d["w"] for d in yearly.values())
|
||||
if all_t == 0: return None
|
||||
yearly_stats = [YearlyStats(y, d["t"], d["w"], d["pnl"]) for y, d in sorted(yearly.items())]
|
||||
return BacktestResult(
|
||||
strategy_name=self.name, asset=asset, timeframe=tf, params=params,
|
||||
trades=all_t, wins=all_w, pnl=sum(d["pnl"] for d in yearly.values()),
|
||||
capital=capital, initial_capital=self.initial_capital,
|
||||
max_dd=max_dd * 100, time_in_market_pct=total_bars / n * 100,
|
||||
avg_trade_duration_h=hold * TF_MINUTES.get(tf, 60) / 60,
|
||||
years_active=len(yearly), yearly=yearly_stats,
|
||||
)
|
||||
|
||||
def report_all_presets(self):
|
||||
"""Esegue tutte le combinazioni preset × asset × tf."""
|
||||
all_results = []
|
||||
for preset_name, filter_list in self.FILTER_PRESETS.items():
|
||||
for asset in self.default_assets:
|
||||
for tf in self.default_timeframes:
|
||||
r = self.backtest(asset, tf, filters=filter_list)
|
||||
if r and r.trades >= 20:
|
||||
r.strategy_name = f"SQ04 {preset_name}"
|
||||
all_results.append(r)
|
||||
|
||||
all_results.sort(key=lambda r: r.accuracy, reverse=True)
|
||||
|
||||
print(f"\n{'=' * 120}")
|
||||
print(f" SQ04 ULTIMATE — TUTTI I PRESET")
|
||||
print(f"{'=' * 120}")
|
||||
for r in all_results:
|
||||
r.print_summary()
|
||||
return all_results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
strategy = SqueezeUltimate()
|
||||
strategy.report_all_presets()
|
||||
Reference in New Issue
Block a user