01c19662cb
Includes core prod + GREEN/BLUE subsystems: - prod/ (BLUE harness, configs, scripts, docs) - nautilus_dolphin/ (GREEN Nautilus-native impl + dvae/ preserved) - adaptive_exit/ (AEM engine + models/bucket_assignments.pkl) - Observability/ (EsoF advisor, TUI, dashboards) - external_factors/ (EsoF producer) - mc_forewarning_qlabs_fork/ (MC regime/envelope) Excludes runtime caches, logs, backups, and reproducible artifacts per .gitignore.
134 lines
5.1 KiB
Python
Executable File
134 lines
5.1 KiB
Python
Executable File
"""
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Boost sweep: find what constant boost+beta gives gold ROI=189.48%.
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Uses actor-style loop, static vol_ok, gidx fix (T=2155 base).
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Tests multiple (boost, beta) pairs to bracket the gold result.
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"""
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import sys, math, pathlib
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import numpy as np
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import pandas as pd
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sys.path.insert(0, '/mnt/dolphinng5_predict')
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sys.path.insert(0, '/mnt/dolphinng5_predict/nautilus_dolphin')
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print("Importing...", flush=True)
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from nautilus_dolphin.nautilus.proxy_boost_engine import create_boost_engine
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print("Import done.", flush=True)
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PARQUET_DIR = pathlib.Path('/mnt/dolphinng5_predict/vbt_cache')
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VOL_P60_INWINDOW = 0.00009868
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INITIAL_CAPITAL = 25000.0
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ENG_KWARGS = dict(
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max_hold_bars=120, min_irp_alignment=0.45, max_leverage=8.0,
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vel_div_threshold=-0.02, vel_div_extreme=-0.05,
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min_leverage=0.5, leverage_convexity=3.0,
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fraction=0.20, fixed_tp_pct=0.0095, stop_pct=1.0,
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use_direction_confirm=True, dc_lookback_bars=7, dc_min_magnitude_bps=0.75,
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dc_skip_contradicts=True, dc_leverage_boost=1.0, dc_leverage_reduce=0.5,
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use_asset_selection=True, use_sp_fees=True, use_sp_slippage=True,
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sp_maker_entry_rate=0.62, sp_maker_exit_rate=0.50,
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use_ob_edge=True, ob_edge_bps=5.0, ob_confirm_rate=0.40,
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lookback=100, use_alpha_layers=True, use_dynamic_leverage=True, seed=42,
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)
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def make_engine():
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eng = create_boost_engine(mode='d_liq', initial_capital=INITIAL_CAPITAL, **ENG_KWARGS)
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eng.set_esoteric_hazard_multiplier(0.0)
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return eng
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def compute_vol_ok(df):
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btc_f = df['BTCUSDT'].values.astype('float64')
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n = len(btc_f)
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vol_ok = np.zeros(n, dtype=bool)
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for j in range(50, n):
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seg = btc_f[max(0, j-50):j]
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diffs = np.diff(seg)
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denom = seg[:-1]
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if np.any(denom == 0):
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continue
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v = float(np.std(diffs / denom))
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if math.isfinite(v) and v > 0:
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vol_ok[j] = v > VOL_P60_INWINDOW
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return vol_ok
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def run_full(boost, beta):
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"""Run 56-day backtest with constant boost+beta injected each day."""
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eng = make_engine()
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files = sorted(PARQUET_DIR.glob('*.parquet'))
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total_T = 0
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for pf in files:
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date_str = pf.stem
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df = pd.read_parquet(pf)
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eng.begin_day(date_str)
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eng._day_base_boost = boost
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eng._day_beta = beta
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data_arr = df.values
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cols = df.columns.tolist()
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vd_idx = cols.index('vel_div') if 'vel_div' in cols else -1
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v50_idx = cols.index('v50_lambda_max_velocity') if 'v50_lambda_max_velocity' in cols else -1
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v750_idx = cols.index('v750_lambda_max_velocity') if 'v750_lambda_max_velocity' in cols else -1
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i50_idx = cols.index('instability_50') if 'instability_50' in cols else -1
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usdt_idxs = [(c, cols.index(c)) for c in cols if c.endswith('USDT')]
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vol_ok = compute_vol_ok(df)
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for i in range(len(df)):
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row_vals = data_arr[i]
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vd_raw = float(row_vals[vd_idx]) if vd_idx != -1 else float('nan')
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if not math.isfinite(vd_raw):
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eng._global_bar_idx += 1
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continue
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v750 = float(row_vals[v750_idx]) if v750_idx != -1 and math.isfinite(float(row_vals[v750_idx])) else 0.0
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inst50 = float(row_vals[i50_idx]) if i50_idx != -1 and math.isfinite(float(row_vals[i50_idx])) else 0.0
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v50 = float(row_vals[v50_idx]) if v50_idx != -1 and math.isfinite(float(row_vals[v50_idx])) else 0.0
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prices = {sym: float(row_vals[ci]) for sym, ci in usdt_idxs
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if math.isfinite(float(row_vals[ci])) and float(row_vals[ci]) > 0}
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prev_pos = eng.position
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if hasattr(eng, 'pre_bar_proxy_update'):
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eng.pre_bar_proxy_update(inst50, v750)
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eng.step_bar(
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bar_idx=i, vel_div=vd_raw, prices=prices,
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v50_vel=v50, v750_vel=v750,
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vol_regime_ok=bool(vol_ok[i]),
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)
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if prev_pos is not None and eng.position is None:
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total_T += 1
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eng.end_day()
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roi = 100 * (eng.capital / INITIAL_CAPITAL - 1)
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return total_T, eng.capital, roi
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def main():
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print(f"\n{'boost':>6} {'beta':>5} {'T':>5} {'cap':>10} {'ROI%':>8}", flush=True)
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print("-" * 40, flush=True)
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# Sweep: boost in [1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2] x beta in [0.0, 0.5, 0.8]
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candidates = [
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(1.0, 0.0), # baseline (no ACB)
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(1.0, 0.5), # beta-only (no boost, mid beta)
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(1.0, 0.8), # beta-high only
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(1.5, 0.5), # signals=2 mid-beta
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(1.5, 0.8), # signals=2 high-beta
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(1.7, 0.5), # signals=3 mid-beta
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(1.7, 0.8), # signals=3 high-beta
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(1.8, 0.5), # signals=4 mid-beta
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(1.8, 0.8), # signals=4 high-beta (max likely)
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(2.0, 0.8), # above max
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(2.2, 0.8), # extrapolate
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]
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for boost, beta in candidates:
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T, cap, roi = run_full(boost, beta)
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marker = ' <-- gold target' if abs(roi - 189.48) < 5 else ''
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print(f"{boost:>6.2f} {beta:>5.1f} {T:>5d} ${cap:>9,.0f} {roi:>7.2f}%{marker}", flush=True)
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print(f"\nGold target: T=2155, ROI=+189.48%", flush=True)
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if __name__ == '__main__':
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main()
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