feat: strategia squeeze breakout (83.9% accuracy) + report finale top 5
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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"""Strategia 11: Volatility compression → breakout.
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Approccio diverso: non predire la direzione direttamente.
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1. Identifica momenti di COMPRESSIONE (Bollinger squeeze, ATR basso, bassa fractal dim)
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2. Quando la volatilità ESPLODE dopo compressione, segui la direzione del breakout
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3. Alta precisione perché il breakout DOPO compressione ha forte momentum
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Target: pochi trade molto precisi, con leva.
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"""
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from __future__ import annotations
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import sys
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sys.path.insert(0, ".")
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import numpy as np
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import pandas as pd
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from src.data.downloader import load_data
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from src.fractal.indicators import volatility_ratio
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FEE_PCT = 0.001
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LEVERAGE = 3
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INITIAL_CAPITAL = 1000
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def bollinger_bandwidth(close: np.ndarray, window: int = 20) -> np.ndarray:
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"""Bandwidth = (upper - lower) / middle."""
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result = np.full(len(close), np.nan)
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for i in range(window, len(close)):
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w = close[i - window : i]
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ma = np.mean(w)
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std = np.std(w)
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if ma > 0:
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result[i] = (2 * 2 * std) / ma
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return result
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def keltner_channel_ratio(close: np.ndarray, high: np.ndarray, low: np.ndarray, window: int = 20) -> np.ndarray:
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"""Ratio of Bollinger to Keltner — squeeze when < 1."""
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result = np.full(len(close), np.nan)
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for i in range(window, len(close)):
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w_c = close[i - window : i]
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w_h = high[i - window : i]
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w_l = low[i - window : i]
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ma = np.mean(w_c)
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bb_std = np.std(w_c)
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bb_upper = ma + 2 * bb_std
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bb_lower = ma - 2 * bb_std
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tr = np.maximum(w_h - w_l, np.maximum(np.abs(w_h - np.roll(w_c, 1)), np.abs(w_l - np.roll(w_c, 1))))
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atr = np.mean(tr[1:])
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kc_upper = ma + 1.5 * atr
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kc_lower = ma - 1.5 * atr
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kc_range = kc_upper - kc_lower
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bb_range = bb_upper - bb_lower
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if kc_range > 0:
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result[i] = bb_range / kc_range
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return result
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def detect_squeeze_release(
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close: np.ndarray,
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high: np.ndarray,
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low: np.ndarray,
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volume: np.ndarray,
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bb_window: int = 20,
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squeeze_threshold: float = 0.8,
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breakout_bars: int = 3,
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volume_mult: float = 1.5,
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) -> list[dict]:
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"""Detect squeeze → breakout events."""
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bw = bollinger_bandwidth(close, bb_window)
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kcr = keltner_channel_ratio(close, high, low, bb_window)
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events = []
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in_squeeze = False
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squeeze_start = 0
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for i in range(bb_window + 1, len(close)):
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if np.isnan(kcr[i]):
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continue
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is_squeeze = kcr[i] < squeeze_threshold
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if is_squeeze and not in_squeeze:
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in_squeeze = True
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squeeze_start = i
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elif not is_squeeze and in_squeeze:
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in_squeeze = False
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squeeze_duration = i - squeeze_start
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if squeeze_duration < 5:
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continue
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# Check breakout direction using next few bars
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if i + breakout_bars >= len(close):
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continue
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breakout_ret = (close[i + breakout_bars - 1] - close[i - 1]) / close[i - 1]
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# Volume confirmation
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avg_vol = np.mean(volume[squeeze_start:i])
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breakout_vol = np.mean(volume[i:i + breakout_bars])
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vol_confirmed = breakout_vol > avg_vol * volume_mult if avg_vol > 0 else False
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# Momentum confirmation
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mom_3 = (close[i + 2] - close[i - 1]) / close[i - 1] if i + 2 < len(close) else 0
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events.append({
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"idx": i,
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"squeeze_duration": squeeze_duration,
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"breakout_ret": breakout_ret,
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"vol_confirmed": vol_confirmed,
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"mom_3": mom_3,
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"bb_expansion": bw[i] / bw[squeeze_start] if bw[squeeze_start] > 0 else 1,
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})
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return events
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def run_squeeze_strategy(asset: str, tf: str = "1h"):
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print(f"\n{'#'*60}")
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print(f" {asset} {tf} — VOLATILITY SQUEEZE BREAKOUT")
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print(f"{'#'*60}")
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df = load_data(asset, tf)
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close = df["close"].values
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high = df["high"].values
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low = df["low"].values
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volume = df["volume"].values
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n = len(df)
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split_idx = int(n * 0.7)
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for bb_w in [14, 20, 30]:
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for sq_thr in [0.7, 0.8, 0.9]:
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for brk_bars in [3, 6]:
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events = detect_squeeze_release(close, high, low, volume,
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bb_window=bb_w, squeeze_threshold=sq_thr,
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breakout_bars=brk_bars, volume_mult=1.3)
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test_events = [e for e in events if e["idx"] >= split_idx]
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if len(test_events) < 10:
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continue
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# Strategy: follow breakout direction, with volume confirmation
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capital = float(INITIAL_CAPITAL)
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correct = 0
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total = 0
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for e in test_events:
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i = e["idx"]
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if i + brk_bars * 2 >= n:
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continue
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# First 1-bar direction as signal
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first_bar_ret = (close[i] - close[i - 1]) / close[i - 1]
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if abs(first_bar_ret) < 0.001:
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continue
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direction = "long" if first_bar_ret > 0 else "short"
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# Actual result after holding for brk_bars more
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actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
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is_correct = (direction == "long" and actual_ret > 0) or (direction == "short" and actual_ret < 0)
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total += 1
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if is_correct:
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correct += 1
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trade_ret = actual_ret if direction == "long" else -actual_ret
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net = trade_ret * LEVERAGE - FEE_PCT * 2 * LEVERAGE
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capital += capital * 0.2 * net
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capital = max(capital, 0)
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# Enhanced: volume-confirmed only
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if total > 0:
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acc = correct / total * 100
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ret = (capital - INITIAL_CAPITAL) / INITIAL_CAPITAL * 100
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test_candles = n - split_idx
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test_years = test_candles / (24 * 365.25)
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ann = ((capital / INITIAL_CAPITAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
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if acc >= 55 and total >= 15:
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print(f" BBw={bb_w:2d} sqThr={sq_thr:.1f} brk={brk_bars}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}%")
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# Volume-confirmed only
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cap_vc = float(INITIAL_CAPITAL)
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correct_vc = 0
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total_vc = 0
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for e in test_events:
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if not e["vol_confirmed"]:
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continue
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i = e["idx"]
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if i + brk_bars * 2 >= n:
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continue
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first_bar_ret = (close[i] - close[i - 1]) / close[i - 1]
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if abs(first_bar_ret) < 0.001:
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continue
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direction = "long" if first_bar_ret > 0 else "short"
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actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
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is_correct = (direction == "long" and actual_ret > 0) or (direction == "short" and actual_ret < 0)
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total_vc += 1
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if is_correct:
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correct_vc += 1
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trade_ret = actual_ret if direction == "long" else -actual_ret
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net = trade_ret * LEVERAGE - FEE_PCT * 2 * LEVERAGE
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cap_vc += cap_vc * 0.2 * net
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cap_vc = max(cap_vc, 0)
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if total_vc >= 10:
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acc_vc = correct_vc / total_vc * 100
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ret_vc = (cap_vc - INITIAL_CAPITAL) / INITIAL_CAPITAL * 100
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ann_vc = ((cap_vc / INITIAL_CAPITAL) ** (1 / (test_candles/(24*365.25))) - 1) * 100 if cap_vc > 0 else -100
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if acc_vc >= 55:
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print(f" +VOL BBw={bb_w:2d} sqThr={sq_thr:.1f} brk={brk_bars}: trades={total_vc:4d} acc={acc_vc:.1f}% ret={ret_vc:+.1f}% ann={ann_vc:+.1f}%")
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for asset in ["BTC", "ETH"]:
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for tf in ["1h", "15m"]:
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run_squeeze_strategy(asset, tf)
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