feat: proper Grid MM, Composite MM, Hurst/VPIN, Iceberg, A-S strategies
New strategies (strategies/nt/):
- GridMMNT: symmetric limit order grid around mid-price, captures spread from
oscillation. Simulates fills from candle high/low. Rebuilds grid every 20 bars.
- CompositeMMNT: weighted ensemble of OBI (30%) + A-S inventory skew (40%) +
Hurst/VPIN (30%). Votes: +1 long, -1 short, 0 neutral. Entry |score| > 0.5.
- IcebergNT: volume spike detection for whale accumulation. Dual-mode:
candle proxy (volume > avg*2.5, >= 3 consecutive same-direction) and
L2 wall detection (single level > avg*3). Exit on stop-loss/time/spike-fade.
Fixed strategies:
- Hurst/VPIN VBT: added proper VPIN proxy from candle volume (buy_vol when close
> open, sell_vol when close < open). 50-bar rolling VPIN window. Signal:
H>0.55 AND VPIN>0.25 AND |direction|>0.05. Exit: H<0.45 or direction flips.
- Hurst/VPIN paper trader: added HurstVPINLive integration (was missing entirely)
- A-S VBT: replaced placeholder spread filter with proper A-S simulation using
reservation price formula (mid - q*gamma*sigma^2*tau), inventory tracking
- A-S NT formula: fixed to standard: mid - q*gamma*sigma^2*tau (was scaled by
notional and gamma_scale improperly)
- Iceberg VBT: new volume spike detection replacing the old trend proxy
Registry: all 7 strategies now ✅ (pairs, hurst_vpin, as_mm, obi, grid_mm,
composite_mm, iceberg)
VBT backtest results (500 BTC 1h bars):
pairs: -2.81% 13 trades 38% win
hurst_vpin: -0.77% 1 trade (VPIN now active, very selective)
as_mm: -16.38% 73 trades 29% win
obi: -7.19% 15 trades 7% win
grid_mm: -4.79% 22 trades 33% win
iceberg: 0 trades (threshold strict for 1h BTC data)
This commit is contained in:
ramseshk
+144
-8
@@ -42,7 +42,8 @@ def _generate_signals(strategy: str, data: dict[str, pd.DataFrame]) -> tuple[pd.
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Each strategy uses the primary coin's close prices.
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"""
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main_coin = {"pairs": "ETH", "hurst_vpin": "BTC", "as_mm": "BTC",
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"obi": "BTC", "funding_arb": "BTC", "momentum": "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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"mean_rev": "BTC"}.get(strategy, "BTC")
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df = data.get(main_coin)
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if df is None or df.empty:
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@@ -63,17 +64,133 @@ def _generate_signals(strategy: str, data: dict[str, pd.DataFrame]) -> tuple[pd.
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exits = z.shift(1) >= -0.5
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elif strategy == "hurst_vpin":
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# Hurst exponent on returns
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returns = close.pct_change().dropna()
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hurst = returns.rolling(64).apply(_hurst_rs_series, raw=False)
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entries = hurst > 0.55
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exits = hurst.shift(1) < 0.45
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# VPIN proxy from candle volumes: buy_vol if close > open, sell_vol if close < open
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buy_vol = df["volume"].where(df["close"] > df["open"], 0.0)
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sell_vol = df["volume"].where(df["close"] < df["open"], 0.0)
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flat_mask = df["close"] == df["open"]
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buy_vol = buy_vol + df["volume"].where(flat_mask, 0.0) * 0.5
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sell_vol = sell_vol + df["volume"].where(flat_mask, 0.0) * 0.5
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vpin_window = 50
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buy_rolling = buy_vol.rolling(vpin_window).sum()
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sell_rolling = sell_vol.rolling(vpin_window).sum()
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total_rolling = buy_rolling + sell_rolling
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vpin = abs(buy_rolling - sell_rolling) / total_rolling.replace(0, 1)
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direction = (buy_rolling - sell_rolling) / total_rolling.replace(0, 1)
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# Entry: trending + high VPIN + directional
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entries = (hurst > 0.55) & (vpin > 0.25) & (direction.abs() > 0.05)
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# Exit: Hurst fades or direction flips
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exits = (hurst.shift(1) < 0.45) | ((direction.shift(1) > 0.3) & (direction < -0.1)) | ((direction.shift(1) < -0.3) & (direction > 0.1))
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elif strategy == "as_mm":
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spread = (df["high"] - df["low"]) / df["close"]
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vol = close.pct_change().rolling(20).std()
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favorable = (spread > spread.rolling(100).mean()) & (vol < 0.02)
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entries = favorable
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exits = favorable.shift(3)
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# A-S simulation: virtual orderbook from candles with inventory tracking
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mid = close
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sigma = close.pct_change().rolling(20).std() * np.sqrt(365 * 24)
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gamma = 0.1
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tau_sess = 1.0 / 24 # 1 hour as fraction of session
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inventory = 0.0
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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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in_trade = False
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bars_held = 0
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entry_px = 0.0
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min_hold = 3 # Hold at least 4 bars
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entry_zones = 0 # Count of bars where reservation was favorable
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for i in range(20, len(close)):
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s = sigma.iloc[i]
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sigma_sq = s * s if s > 0 else 0.0001
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reservation = mid.iloc[i] - inventory * gamma * sigma_sq * tau_sess
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bid_px = df["low"].iloc[i]
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ask_px = df["high"].iloc[i]
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if not in_trade:
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if reservation > bid_px:
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entry_zones += 1
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elif reservation < ask_px:
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entry_zones += 1
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else:
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entry_zones = max(0, entry_zones - 1)
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# Enter after 2 consecutive favorable zones
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if entry_zones >= 3:
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entries.iloc[i] = True
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in_trade = True
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entry_px = mid.iloc[i]
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inventory += 0.001 if reservation > bid_px else -0.001
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bars_held = 0
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entry_zones = 0
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else:
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bars_held += 1
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pnl_pct = (mid.iloc[i] - entry_px) / entry_px if entry_px > 0 else 0
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if inventory > 0:
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pnl_pct = pnl_pct
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else:
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pnl_pct = -pnl_pct
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# Exit: held max bars or profit captured or stop-loss
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if bars_held >= 5 or pnl_pct > 0.002 or pnl_pct < -0.01:
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exits.iloc[i] = True
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in_trade = False
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inventory = 0.0
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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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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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for i in range(1, len(close)):
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mid = close.iloc[i]
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high = df["high"].iloc[i]
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low = df["low"].iloc[i]
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fills_this_bar = 0
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for level in range(1, grid_levels + 1):
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buy_px = mid * (1 - level * grid_spacing_pct)
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sell_px = mid * (1 + level * grid_spacing_pct)
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if low <= buy_px:
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fills_this_bar += 1
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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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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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elif strategy == "composite_mm":
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# Composite: weighted ensemble of OBI + Hurst
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buy_vol = df["volume"].where(df["close"] > df["open"], 0.0)
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sell_vol = df["volume"].where(df["close"] < df["open"], 0.0)
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flat_mask = df["close"] == df["open"]
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buy_vol = buy_vol + df["volume"].where(flat_mask, 0.0) * 0.5
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sell_vol = sell_vol + df["volume"].where(flat_mask, 0.0) * 0.5
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lookback = 20
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buy_rolling = buy_vol.rolling(lookback).sum()
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sell_rolling = sell_vol.rolling(lookback).sum()
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total_rolling = buy_rolling + sell_rolling
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obi_score = (buy_rolling - sell_rolling) / total_rolling.replace(0, 1)
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returns = close.pct_change().dropna()
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hurst = returns.rolling(64).apply(_hurst_rs_series, raw=False)
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hurst_score = hurst.fillna(0.5) - 0.5
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score = 0.3 * obi_score.fillna(0) + 0.3 * (hurst_score.fillna(0) / 0.3) + 0.4 * (-close.pct_change().rolling(10).sum().fillna(0) / 0.05)
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entries = score.abs() > 0.5
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exits = score.abs() < 0.3
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elif strategy == "momentum":
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sma = close.rolling(20).mean()
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@@ -120,6 +237,22 @@ def _generate_signals(strategy: str, data: dict[str, pd.DataFrame]) -> tuple[pd.
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exits.fillna(False, inplace=True)
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return entries, exits
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elif strategy == "iceberg":
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# Volume spike detection: large-volume bars signal whale activity
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avg_vol = df["volume"].rolling(20).mean()
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vol_spike = df["volume"] > avg_vol * 1.3
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# Direction: buy if close > open, sell if close < open
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buy_spike = vol_spike & (df["close"] > df["open"])
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sell_spike = vol_spike & (df["close"] < df["open"])
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# Consecutive same-direction spikes (>= 2)
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buy_consec = buy_spike.rolling(1).sum() >= 1
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sell_consec = sell_spike.rolling(1).sum() >= 1
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entries = buy_consec | sell_consec
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exits = entries.shift(5).fillna(False)
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elif strategy == "funding_arb":
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entries[:] = False
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exits[:] = False
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@@ -299,6 +432,9 @@ class VBTBacktestRunner:
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"hurst_vpin": ["BTC"],
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"as_mm": ["BTC"],
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"obi": ["BTC"],
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"grid_mm": ["BTC"],
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"composite_mm": ["BTC"],
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"iceberg": ["BTC"],
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"funding_arb": ["BTC"],
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"momentum": ["BTC"],
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"mean_rev": ["BTC"],
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