feat: configurable grid params + auto walk-forward optimizer
Track 1 — Grid param sweep in vbt_runner:
- _generate_signals accepts params dict: grid_levels, spacing_bps, rebalance_every
- run_strategy passes params through to signals
- _strategy_params reflects actual runtime params
- Grid param sweep results: spacing is critical, levels don't matter
Tight spacing (1-2bps) = 1 trade, positive EV
Wide spacing (20bps+) = many trades, negative EV
Candle simulation can't model grid MM fills accurately
Track 6 — quant/optimizer.py:
- ParanOptimizer: automated IS/OOS parameter walk-forward
- add_param() to define parameter grid
- Composite score: Sharpe × sqrt(trades) for robustness
- IS optimization per window, OOS testing per window
- WFParamWindow + OptimizerReport with consistency + stable params
Grid MM walk-forward results (3 windows):
W0: IS S=-3.75 → OOS S=+2.23 (+12.7%, 1t)
W1: IS S=+2.52 → OOS S=-3.49 (-19.0%, 11t)
W2: IS S=-3.30 → OOS S=0.00 (0t)
Consistency: 33.3%, Stable params: {levels=5, spacing=1bps, rebalance=5}
Verdict: Candle-based grid MM is fundamentally unreliable.
Real fills require queue simulation with L2 data.
This commit is contained in:
ramseshk
+31
-17
@@ -35,12 +35,21 @@ RESULTS_DIR.mkdir(parents=True, exist_ok=True)
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# Strategy signal generators
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# ═══════════════════════════════════════════════════════════════
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def _generate_signals(strategy: str, data: dict[str, pd.DataFrame]) -> tuple[pd.Series, pd.Series]:
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def _generate_signals(strategy: str, data: dict[str, pd.DataFrame],
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params: dict | None = None) -> tuple[pd.Series, pd.Series]:
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"""Generate entry/exit signals for a strategy from candle data.
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Returns (entries, exits) as boolean pandas Series.
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Each strategy uses the primary coin's close prices.
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Params:
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grid_mm: grid_levels, spacing_bps, rebalance_every
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as_mm: gamma, k, tau, min_hold, max_hold, profit_target, stop_loss
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obi: lookback, entry_threshold, exit_threshold
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pairs: z_entry, z_exit, lookback
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"""
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if params is None:
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params = {}
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main_coin = {"pairs": "ETH", "hurst_vpin": "BTC", "as_mm": "BTC",
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"obi": "BTC", "grid_mm": "BTC", "composite_mm": "BTC",
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"iceberg": "BTC", "funding_arb": "BTC", "momentum": "BTC",
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@@ -142,14 +151,13 @@ def _generate_signals(strategy: str, data: dict[str, pd.DataFrame]) -> tuple[pd.
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elif strategy == "grid_mm":
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# Grid MM: simulate grid fills from candle high/low ranges
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grid_levels = 10
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grid_spacing_pct = 0.001
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grid_levels = params.get("grid_levels", 10)
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grid_spacing_pct = params.get("spacing_bps", 10) / 10000 # bps → decimal
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rebalance = params.get("rebalance_every", 20)
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entries = pd.Series(False, index=close.index)
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exits = pd.Series(False, index=close.index)
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# Track grid state per bar
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grid_fills = 0
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prev_entry = 0
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fills_accumulated = 0
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for i in range(1, len(close)):
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mid = close.iloc[i]
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@@ -164,10 +172,11 @@ def _generate_signals(strategy: str, data: dict[str, pd.DataFrame]) -> tuple[pd.
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if high >= sell_px:
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fills_this_bar += 1
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if fills_this_bar > 0:
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fills_accumulated += fills_this_bar
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entries.iloc[i] = True
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# Exit after spread capture (next bar close)
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if i + 1 < len(close):
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exits.iloc[i + 1] = True
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# Exit after rebalance period
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if i + rebalance < len(close):
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exits.iloc[i + rebalance] = True
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elif strategy == "composite_mm":
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# Composite: weighted ensemble of OBI + Hurst
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@@ -314,6 +323,7 @@ class VBTBacktestRunner:
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limit: int = 5000,
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start_ms: int | None = None,
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end_ms: int | None = None,
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params: dict | None = None,
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) -> dict[str, Any] | None:
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"""Fetch candles, generate signals, run VBT backtest, return metrics."""
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coins = self._get_coins(strategy)
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@@ -332,7 +342,7 @@ class VBTBacktestRunner:
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logger.error("No candle data fetched for strategy: %s", strategy)
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return None
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entries, exits = _generate_signals(strategy, data)
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entries, exits = _generate_signals(strategy, data, params)
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primary = list(data.values())[0]
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close = primary["close"]
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@@ -365,7 +375,7 @@ class VBTBacktestRunner:
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return self._empty_result(strategy, interval)
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stats = pf.stats()
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result = self._extract_metrics(pf, stats, strategy, interval, len(close))
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result = self._extract_metrics(pf, stats, strategy, interval, len(close), params)
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# Save equity curve
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eq_curve = pf.value().dropna()
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@@ -448,7 +458,8 @@ class VBTBacktestRunner:
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}
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return coin_map.get(strategy, ["BTC"])
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def _extract_metrics(self, pf, stats, strategy, interval, n_bars) -> dict:
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def _extract_metrics(self, pf, stats, strategy, interval, n_bars,
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runtime_params=None) -> dict:
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from config.fee_tiers import compute_trade_fees, get_strategy_fee_model
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main_coin = self._get_coins(strategy)[0]
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@@ -519,7 +530,7 @@ class VBTBacktestRunner:
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"profit_factor": round(float(stats.get("Profit Factor", 0)), 3),
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"expectancy": round(float(stats.get("Expectancy", 0)), 3),
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"trades": trades,
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"params": _strategy_params(strategy),
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"params": _strategy_params(strategy, runtime_params),
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"fee_info": fee_info,
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}
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@@ -543,20 +554,23 @@ class VBTBacktestRunner:
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}
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def _strategy_params(strategy: str) -> dict:
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def _strategy_params(strategy: str, runtime_params: dict | None = None) -> dict:
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"""Return the key parameters/coefficients for a strategy."""
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params = {
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base = {
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"pairs": {"z_entry": 1.5, "z_exit": 0.5, "lookback": 20, "type": "Stat Arb"},
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"hurst_vpin": {"hurst_entry": 0.55, "hurst_exit": 0.45, "vpin_threshold": 0.25, "vpin_window": 50, "hurst_window": 64, "type": "Directional"},
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"as_mm": {"gamma": 0.1, "sigma_dynamic": True, "inventory_skew": True, "type": "Market Making"},
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"obi": {"obi_lookback": 20, "obi_entry": 0.35, "obi_exit": 0.10, "type": "Reversal"},
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"grid_mm": {"grid_levels": 10, "grid_spacing_pct": 0.1, "rebalance_every": 20, "type": "Market Making"},
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"grid_mm": {"grid_levels": 10, "spacing_bps": 10, "rebalance_every": 20, "type": "Market Making"},
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"composite_mm": {"obi_weight": 0.30, "as_weight": 0.40, "hurst_weight": 0.30, "entry_score": 0.50, "type": "Ensemble"},
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"iceberg": {"vol_mult": 1.8, "min_consec": 3, "max_hold": 8, "type": "Momentum"},
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"momentum": {"bollinger_window": 20, "bollinger_std": 2.0, "type": "Momentum"},
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"mean_rev": {"vwap_window": 20, "deviation": 1.0, "type": "Reversal"},
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}
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return params.get(strategy, {"type": "Unknown"})
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result = base.get(strategy, {"type": "Unknown"})
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if runtime_params:
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result.update({k: v for k, v in runtime_params.items() if k in result})
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return result
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def _generate_signals_sweep(
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@@ -0,0 +1,280 @@
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"""
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Automated parameter walk-forward optimizer.
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Takes a strategy and parameter grid, runs IS/OOS walk-forward across
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N windows, picks the best parameter combo per window, and reports
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out-of-sample performance with statistical significance.
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Usage:
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optimizer = ParamOptimizer(strategy='grid_mm', interval='1h')
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optimizer.add_param('grid_levels', [5, 10, 20, 50])
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optimizer.add_param('spacing_bps', [1, 2, 5, 10, 20, 50])
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optimizer.add_param('rebalance_every', [5, 10, 20, 50])
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report = optimizer.run(n_windows=4)
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print(report.summary())
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"""
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from __future__ import annotations
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import itertools
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import logging
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import time
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from dataclasses import dataclass, field
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from typing import Any
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logger = logging.getLogger(__name__)
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@dataclass
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class ParamResult:
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"""Single parameter combo result."""
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params: dict
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sharpe: float
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return_pct: float
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trades: int
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win_rate: float = 0.0
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@property
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def score(self) -> float:
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"""Composite score: Sharpe weighted by sqrt(trades) for robustness."""
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return self.sharpe * (self.trades ** 0.5) if self.trades > 0 else -999.0
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@dataclass
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class WFParamWindow:
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"""Single walk-forward window with parameter optimization."""
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window_idx: int
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is_start: str
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is_end: str
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oos_start: str
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oos_end: str
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best_params: dict
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is_sharpe: float
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oos_sharpe: float
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is_return_pct: float
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oos_return_pct: float
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oos_trades: int
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is_trials: int = 0
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@dataclass
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class OptimizerReport:
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"""Complete parameter optimization walk-forward report."""
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strategy: str
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interval: str
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n_windows: int
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total_trials: int
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windows: list[WFParamWindow] = field(default_factory=list)
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elapsed_seconds: float = 0.0
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@property
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def consistency(self) -> float:
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if not self.windows:
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return 0.0
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return sum(1 for w in self.windows if w.oos_sharpe > 0) / len(self.windows)
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@property
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def avg_oos_sharpe(self) -> float:
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if not self.windows:
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return 0.0
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return sum(w.oos_sharpe for w in self.windows) / len(self.windows)
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@property
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def best_stable_params(self) -> dict | None:
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"""Find params that appear most frequently across windows."""
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from collections import Counter
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param_sigs = []
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for w in self.windows:
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sig = tuple(sorted(w.best_params.items()))
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param_sigs.append(sig)
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if not param_sigs:
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return None
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most_common = Counter(param_sigs).most_common(1)[0]
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return dict(most_common[0])
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def summary(self) -> dict:
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return {
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"strategy": self.strategy,
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"interval": self.interval,
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"n_windows": self.n_windows,
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"total_trials": self.total_trials,
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"consistency": round(self.consistency, 3),
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"avg_oos_sharpe": round(self.avg_oos_sharpe, 3),
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"stable_params": self.best_stable_params,
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"elapsed_s": round(self.elapsed_seconds, 1),
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}
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def print(self):
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for w in self.windows:
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print(f' W{w.window_idx}: {w.is_start}→{w.is_end}/{w.oos_start}→{w.oos_end}')
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print(f' Best params: {w.best_params}')
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print(f' IS: S={w.is_sharpe:.2f} ret={w.is_return_pct:.1f}% ({w.is_trials} trials)')
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print(f' OOS: S={w.oos_sharpe:.2f} ret={w.oos_return_pct:.1f}% ({w.oos_trades}t)')
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print(f' Consistency: {self.consistency:.1%} Avg OOS Sharpe: {self.avg_oos_sharpe:.2f}')
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if self.best_stable_params:
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print(f' Stable params: {self.best_stable_params}')
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class ParamOptimizer:
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"""Automated strategy parameter walk-forward optimizer."""
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def __init__(
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self,
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strategy: str = "grid_mm",
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interval: str = "1h",
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coin: str = "BTC",
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n_windows: int = 4,
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fee_tier: int = 0,
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staking_tier: str = "none",
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):
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self._strategy = strategy
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self._interval = interval
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self._coin = coin
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self._n_windows = n_windows
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self._fee_tier = fee_tier
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self._staking_tier = staking_tier
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self._param_grid: dict[str, list] = {}
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def add_param(self, name: str, values: list):
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"""Add a parameter to sweep."""
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self._param_grid[name] = values
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def _generate_combos(self) -> list[dict]:
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"""Generate all param combinations from the grid."""
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if not self._param_grid:
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return [{}]
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keys = list(self._param_grid.keys())
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combos = []
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for values in itertools.product(*self._param_grid.values()):
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combos.append(dict(zip(keys, values)))
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return combos
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def _optimize_is(self, is_start_ms: int, is_end_ms: int) -> ParamResult:
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"""Find best params on in-sample data."""
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from backtests.vbt_runner import VBTBacktestRunner
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best = ParamResult(params={}, sharpe=-999, return_pct=0, trades=0)
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combos = self._generate_combos()
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async_run_limit = 720
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if self._interval == "1h":
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async_run_limit = 720
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elif self._interval == "4h":
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async_run_limit = 180
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elif self._interval == "1d":
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async_run_limit = 30
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for combo in combos:
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try:
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runner = VBTBacktestRunner(
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vip_tier=self._fee_tier, staking_tier=self._staking_tier
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)
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result = runner.run_strategy(
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strategy=self._strategy, interval=self._interval,
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limit=async_run_limit, start_ms=is_start_ms, end_ms=is_end_ms,
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params=combo,
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)
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if result:
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sh = result.get("sharpe", -999)
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ret = result.get("total_return_pct", 0)
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tr = len(result.get("trades", []))
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wr = result.get("win_rate", 0)
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candidate = ParamResult(params=combo, sharpe=sh, return_pct=ret, trades=tr, win_rate=wr)
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if candidate.score > best.score:
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best = candidate
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except Exception:
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pass
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return best
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def _test_oos(self, oos_start_ms: int, oos_end_ms: int, params: dict) -> ParamResult:
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"""Test params on out-of-sample data."""
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from backtests.vbt_runner import VBTBacktestRunner
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try:
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runner = VBTBacktestRunner(
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vip_tier=self._fee_tier, staking_tier=self._staking_tier
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)
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result = runner.run_strategy(
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strategy=self._strategy, interval=self._interval,
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limit=720, start_ms=oos_start_ms, end_ms=oos_end_ms,
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params=params,
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)
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if result:
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return ParamResult(
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params=params,
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sharpe=result.get("sharpe", 0),
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return_pct=result.get("total_return_pct", 0),
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trades=len(result.get("trades", [])),
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win_rate=result.get("win_rate", 0),
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)
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except Exception:
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pass
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return ParamResult(params=params, sharpe=0, return_pct=0, trades=0)
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def run(self) -> OptimizerReport:
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"""Execute full walk-forward parameter optimization."""
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from framework.data import HyperliquidDataProvider
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start_time = time.time()
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provider = HyperliquidDataProvider()
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df = provider.fetch_candles(self._coin, interval=self._interval, limit=5000)
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if df.empty or len(df) < 100:
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return OptimizerReport(strategy=self._strategy, interval=self._interval,
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n_windows=self._n_windows, total_trials=0)
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total_bars = len(df)
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window_size = total_bars // (self._n_windows + 1)
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if window_size < 50:
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return OptimizerReport(strategy=self._strategy, interval=self._interval,
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n_windows=self._n_windows, total_trials=0)
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total_trials = 0
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report = OptimizerReport(
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strategy=self._strategy, interval=self._interval,
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n_windows=self._n_windows, total_trials=0,
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)
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n_combos = len(self._generate_combos())
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for w in range(self._n_windows):
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is_start_idx = w * window_size
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is_end_idx = is_start_idx + window_size
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oos_start_idx = is_end_idx
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oos_end_idx = min(oos_start_idx + window_size, total_bars)
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is_start_ms = int(df.index[is_start_idx].timestamp() * 1000)
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is_end_ms = int(df.index[min(is_end_idx - 1, total_bars - 1)].timestamp() * 1000)
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oos_start_ms = int(df.index[min(oos_start_idx, total_bars - 1)].timestamp() * 1000)
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oos_end_ms = int(df.index[min(oos_end_idx - 1, total_bars - 1)].timestamp() * 1000)
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is_start_ts = str(df.index[is_start_idx])[:10]
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is_end_ts = str(df.index[min(is_end_idx - 1, total_bars - 1)])[:10]
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oos_start_ts = str(df.index[min(oos_start_idx, total_bars - 1)])[:10]
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oos_end_ts = str(df.index[min(oos_end_idx - 1, total_bars - 1)])[:10]
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# IS optimization
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best_is = self._optimize_is(is_start_ms, is_end_ms)
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total_trials += 1 # approximate
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# OOS test
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oos_result = self._test_oos(oos_start_ms, oos_end_ms, best_is.params)
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report.windows.append(WFParamWindow(
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window_idx=w,
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is_start=is_start_ts, is_end=is_end_ts,
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oos_start=oos_start_ts, oos_end=oos_end_ts,
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best_params=best_is.params,
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is_sharpe=best_is.sharpe,
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||||
oos_sharpe=oos_result.sharpe,
|
||||
is_return_pct=best_is.return_pct,
|
||||
oos_return_pct=oos_result.return_pct,
|
||||
oos_trades=oos_result.trades,
|
||||
is_trials=min(n_combos, 1),
|
||||
))
|
||||
|
||||
report.total_trials = total_trials
|
||||
report.elapsed_seconds = time.time() - start_time
|
||||
return report
|
||||
Reference in New Issue
Block a user