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
@@ -181,6 +181,9 @@ class NTBacktestRunner:
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"hurst_vpin": "strategies.nt.hurst_vpin_nt.HurstVPINNT",
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"as_mm": "strategies.nt.as_mm_nt.ASMarketMakingNT",
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"obi": "strategies.nt.obi_nt.OBINT",
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"grid_mm": "strategies.nt.grid_mm_nt.GridMMNT",
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"composite_mm": "strategies.nt.composite_mm_nt.CompositeMMNT",
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"iceberg": "strategies.nt.iceberg_nt.IcebergNT",
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}
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path = registry.get(strategy)
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if not path:
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+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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@@ -574,6 +574,9 @@ async def list_vbt_strategies():
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{"key": "hurst_vpin", "name": "Hurst VPIN", "coins": ["BTC"]},
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{"key": "as_mm", "name": "Avellaneda-Stoikov MM", "coins": ["BTC"]},
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{"key": "obi", "name": "Order Book Imbalance", "coins": ["BTC"]},
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{"key": "grid_mm", "name": "Grid Market Making", "coins": ["BTC"]},
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{"key": "composite_mm", "name": "Composite MM", "coins": ["BTC"]},
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{"key": "iceberg", "name": "Iceberg Detection", "coins": ["BTC"]},
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{"key": "momentum", "name": "Momentum Breakout", "coins": ["ETH"]},
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{"key": "mean_rev", "name": "Mean Reversion", "coins": ["ETH"]},
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])
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@@ -47,6 +47,21 @@ STRATEGY_REGISTRY = {
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"description": "Volume-weighted bid/ask skew — enters when L2 imbalance heavy",
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"class": "strategies.nt.obi_nt.OBINT",
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},
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"grid_mm": {
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"name": "Grid Market Making",
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"description": "Symmetric grid of limit orders around mid — captures oscillation",
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"class": "strategies.nt.grid_mm_nt.GridMMNT",
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},
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"composite_mm": {
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"name": "Composite Market Making",
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"description": "Weighted ensemble of OBI + A-S + Hurst/VPIN signals",
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"class": "strategies.nt.composite_mm_nt.CompositeMMNT",
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},
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"iceberg": {
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"name": "Iceberg Detection",
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"description": "Volume spike detection — follows whale accumulation patterns",
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"class": "strategies.nt.iceberg_nt.IcebergNT",
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},
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"funding_arb": {
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"name": "Funding Rate Arb",
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"description": "Delta-neutral carry — collect funding payments",
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@@ -87,6 +87,13 @@ STRATEGIES = {
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"signals": [], "type": "reversal", "size":0.022500, "fee_model": "taker",
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"description": "VWAP deviation — buys below VWAP, sells above. Oscillates around fair value.",
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},
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"Hurst VPIN": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "momentum", "size": 0.002, "fee_model": "taker",
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"description": "Hurst exponent regime filter + VPIN informed flow. Enters when both align trending + high flow imbalance.",
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},
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"Hawkes OFI (new)": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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@@ -228,6 +235,24 @@ def compute_signals():
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# Order Book Imbalance — MOVED to main loop (uses real L2 bid/ask volume)
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# Hurst/VPIN — feed BTC price into dollar bars
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if len(btc_prices) >= 3:
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try:
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from strategies.hurst_vpin_live import HurstVPINLive
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if "_hv_live" not in dir():
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globals()["_hv_live"] = HurstVPINLive(
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threshold=50000.0, hurst_entry=0.55, vpin_threshold=0.25
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)
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hv_signal = globals()["_hv_live"].feed_price(btc)
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if hv_signal:
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STRATEGIES["Hurst VPIN"]["signals"].append({
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"time": time.time(),
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"signal": hv_signal["signal"],
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"strength": hv_signal["hurst"],
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"reason": f"H={hv_signal['hurst']:.2f}_V={hv_signal['vpin']:.2f}"
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})
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except: pass
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# Iceberg
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if len(btc_prices) >= 10:
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up = sum(1 for i in range(-9,0) if btc_prices[i+1] > btc_prices[i])
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@@ -7,5 +7,9 @@ from strategies.nt.pairs_trading_nt import PairsTradingNT
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from strategies.nt.hurst_vpin_nt import HurstVPINNT
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from strategies.nt.as_mm_nt import ASMarketMakingNT
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from strategies.nt.obi_nt import OBINT
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from strategies.nt.grid_mm_nt import GridMMNT
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from strategies.nt.composite_mm_nt import CompositeMMNT
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from strategies.nt.iceberg_nt import IcebergNT
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__all__ = ["PairsTradingNT", "HurstVPINNT", "ASMarketMakingNT", "OBINT"]
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__all__ = ["PairsTradingNT", "HurstVPINNT", "ASMarketMakingNT", "OBINT",
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"GridMMNT", "CompositeMMNT", "IcebergNT"]
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@@ -112,10 +112,8 @@ class ASMarketMakingNT(BaseHlStrategy):
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# Reservation price from A-S formula
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q_notional = self._inventory * mid
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gamma_eff = self._gamma * self._gamma_scale
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tau_rem = max(self._tau - t, 0.01)
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sigma_sq = max(self._sigma ** 2, 0.000001)
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reservation = mid - q_notional * gamma_eff * sigma_sq * tau_rem
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reservation = mid - self._inventory * self._gamma * sigma_sq * self._tau
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# Quote sides based on reservation vs market
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quote_bid = reservation >= best_bid or abs(self._inventory) < self._max_inventory * 0.1
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@@ -0,0 +1,252 @@
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"""
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Composite Market Making — weighted ensemble of OBI, A-S, and Hurst/VPIN.
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Each sub-strategy votes: +1 (long), -1 (short), 0 (neutral).
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Weighted score > entry_threshold → enter. Score crosses below exit_threshold → exit.
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Weights (configurable):
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- OBI (30%): volume-based order book imbalance
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- A-S (40%): inventory risk aversion — net short → buy bias, net long → sell bias
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- Hurst/VPIN (30%): trending regime + informed flow direction
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Entry: |weighted_score| > 0.5
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Exit: |weighted_score| < 0.3
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Stop-loss: 2%, take-profit: 2x fee, cooldown: 3 bars
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"""
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from __future__ import annotations
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import logging
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from typing import Any
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import numpy as np
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from nautilus_trader.model.data import Bar
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from nautilus_trader.model.enums import OrderSide
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from framework.base_strategy import BaseHlStrategy
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from framework.config import StrategyConfig
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logger = logging.getLogger(__name__)
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class CompositeMMNT(BaseHlStrategy):
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"""Weighted ensemble of multiple signal sources for market making."""
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def __init__(self, config: StrategyConfig):
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super().__init__(config)
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# Weights (must sum to 1.0 for easy interpretation)
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self._w_obi = config.params.get("obi_weight", 0.30)
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self._w_as = config.params.get("as_weight", 0.40)
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self._w_hurst = config.params.get("hurst_weight", 0.30)
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self._entry_score = config.params.get("entry_score", 0.50)
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self._exit_score = config.params.get("exit_score", 0.30)
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self._stop_loss_pct = config.params.get("stop_loss_pct", 0.02)
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self._take_profit_pct = config.params.get("take_profit_pct", 0.005)
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self._cooldown_bars = config.params.get("cooldown_bars", 3)
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# Sub-strategy instances (lazy)
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self._obi = None
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self._as_mm = None
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self._hurst = None
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# State
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self._bars_since_trade = self._cooldown_bars
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self._in_trade = False
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self._trade_direction: str | None = None
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self._entry_price: float = 0.0
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self._inventory: float = 0.0
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# ── Lazy sub-strategy init ──────────────────────────────────
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def _init_obi(self):
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if self._obi is None:
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from strategies.nt.obi_nt import OBINT
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obi_cfg = StrategyConfig(
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name="OBI-sub", asset=self._cfg.asset,
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instrument=self._cfg.instrument, allocation=self._cfg.allocation,
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order_size=self._cfg.order_size, fee_model="taker",
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params={"obi_lookback": 20, "obi_entry": 0.30, "obi_exit": 0.10, "cooldown_bars": 0},
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||||
)
|
||||
self._obi = OBINT(obi_cfg)
|
||||
|
||||
def _init_as(self):
|
||||
if self._as_mm is None:
|
||||
from strategies.nt.as_mm_nt import ASMarketMakingNT
|
||||
as_cfg = StrategyConfig(
|
||||
name="AS-sub", asset=self._cfg.asset,
|
||||
instrument=self._cfg.instrument, allocation=self._cfg.allocation,
|
||||
order_size=self._cfg.order_size, fee_model="maker",
|
||||
params={"gamma": 0.1, "max_inventory": self._cfg.order_size * 10},
|
||||
)
|
||||
self._as_mm = ASMarketMakingNT(as_cfg)
|
||||
|
||||
def _init_hurst(self):
|
||||
if self._hurst is None:
|
||||
from strategies.nt.hurst_vpin_nt import HurstVPINNT
|
||||
hv_cfg = StrategyConfig(
|
||||
name="HV-sub", asset=self._cfg.asset,
|
||||
instrument=self._cfg.instrument, allocation=self._cfg.allocation,
|
||||
order_size=self._cfg.order_size, fee_model="taker",
|
||||
params={"hurst_window": 64, "hurst_entry": 0.55,
|
||||
"vpin_threshold": 0.25, "dollar_threshold": 100000.0},
|
||||
)
|
||||
self._hurst = HurstVPINNT(hv_cfg)
|
||||
|
||||
# ── Bar handler ─────────────────────────────────────────────
|
||||
|
||||
def on_bar(self, bar: Bar):
|
||||
price = float(bar.close)
|
||||
self._prices.append(price)
|
||||
|
||||
# Feed all sub-strategies
|
||||
self._init_obi()
|
||||
self._init_as()
|
||||
self._init_hurst()
|
||||
|
||||
# Feed bar to sub-strategies (they accumulate state internally)
|
||||
self._obi.on_bar(bar)
|
||||
self._as_mm.on_bar(bar)
|
||||
self._hurst.on_bar(bar)
|
||||
|
||||
self._bars_since_trade += 1
|
||||
|
||||
# Exit check
|
||||
if self._in_trade:
|
||||
if self._check_exit(price):
|
||||
return
|
||||
return
|
||||
|
||||
if self._bars_since_trade < self._cooldown_bars:
|
||||
return
|
||||
|
||||
# Compute ensemble signal
|
||||
signal = self._compute_ensemble()
|
||||
if signal:
|
||||
self._last_signal = signal
|
||||
self.handle_signal(signal)
|
||||
|
||||
# ── Ensemble computation ────────────────────────────────────
|
||||
|
||||
def _compute_ensemble(self) -> dict | None:
|
||||
# OBI vote
|
||||
obi_vote = 0.0
|
||||
obi_sig = self._obi._compute_obi_signal()
|
||||
if obi_sig:
|
||||
obi_vote = 1.0 if "BUY" in obi_sig["signal"] else -1.0
|
||||
|
||||
# A-S vote: inventory skew = -sign(inventory)
|
||||
as_vote = 0.0
|
||||
as_inventory = self._as_mm._inventory
|
||||
max_inv = self._as_mm._max_inventory
|
||||
if max_inv > 0:
|
||||
as_vote = -as_inventory / max_inv # +1 when deeply short, -1 when deeply long
|
||||
|
||||
# Hurst vote
|
||||
hurst_vote = 0.0
|
||||
hv_sig = self._hurst._compute_hurst_vpin_signal()
|
||||
if hv_sig:
|
||||
hurst_vote = 1.0 if "BUY" in hv_sig["signal"] else -1.0
|
||||
|
||||
score = self._w_obi * obi_vote + self._w_as * as_vote + self._w_hurst * hurst_vote
|
||||
|
||||
if abs(score) >= self._entry_score:
|
||||
self._in_trade = True
|
||||
self._trade_direction = "long" if score > 0 else "short"
|
||||
self._entry_price = self._prices[-1] if self._prices else 0.0
|
||||
self._bars_since_trade = 0
|
||||
|
||||
return {
|
||||
"signal": "BUY" if score > 0 else "SELL",
|
||||
"strength": abs(score) / self._entry_score,
|
||||
"score": round(score, 3),
|
||||
"votes": f"obi={obi_vote:.1f}_as={as_vote:.2f}_hurst={hurst_vote:.1f}",
|
||||
"reason": "composite_ensemble",
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
# ── Exit logic ──────────────────────────────────────────────
|
||||
|
||||
def _check_exit(self, current_price: float) -> bool:
|
||||
if not self._in_trade or self._entry_price <= 0:
|
||||
return False
|
||||
|
||||
change_pct = (current_price - self._entry_price) / self._entry_price
|
||||
pnl_pct = change_pct if self._trade_direction == "long" else -change_pct
|
||||
|
||||
exit_reason = None
|
||||
|
||||
if pnl_pct <= -self._stop_loss_pct:
|
||||
exit_reason = "stop_loss"
|
||||
elif pnl_pct >= self._take_profit_pct:
|
||||
exit_reason = "take_profit"
|
||||
elif abs(self._weighted_score_fast()) < self._exit_score:
|
||||
exit_reason = "score_reverted"
|
||||
|
||||
if exit_reason is None:
|
||||
return False
|
||||
|
||||
exit_side = "SELL" if self._trade_direction == "long" else "BUY"
|
||||
self._last_signal = {
|
||||
"signal": exit_side,
|
||||
"strength": abs(pnl_pct) / self._stop_loss_pct,
|
||||
"pnl_pct": round(pnl_pct * 100, 2),
|
||||
"reason": exit_reason,
|
||||
}
|
||||
self._in_trade = False
|
||||
self._trade_direction = None
|
||||
self.handle_signal(self._last_signal)
|
||||
return True
|
||||
|
||||
def _weighted_score_fast(self) -> float:
|
||||
"""Fast ensemble score (no sub-signal computation, just state)."""
|
||||
as_inv = self._as_mm._inventory
|
||||
max_inv = self._as_mm._max_inventory
|
||||
as_vote = -as_inv / max_inv if max_inv > 0 else 0.0
|
||||
|
||||
obi_list = list(self._obi._buy_volumes) if self._obi and self._obi._buy_volumes else []
|
||||
sell_list = list(self._obi._sell_volumes) if self._obi and self._obi._sell_volumes else []
|
||||
obi_vote = 0.0
|
||||
total_buy = sum(obi_list[-10:]) if obi_list else 0
|
||||
total_sell = sum(sell_list[-10:]) if sell_list else 0
|
||||
total = total_buy + total_sell
|
||||
if total > 0:
|
||||
obi_vote = (total_buy - total_sell) / total
|
||||
|
||||
return self._w_obi * obi_vote + self._w_as * as_vote
|
||||
|
||||
# ── Signal (for paper trader) ───────────────────────────────
|
||||
|
||||
def compute_signal(self, price: float | None = None,
|
||||
orderbook: dict | None = None) -> dict | None:
|
||||
if price is None or price <= 0:
|
||||
return None
|
||||
self._prices.append(price)
|
||||
self._init_obi()
|
||||
self._init_as()
|
||||
self._init_hurst()
|
||||
|
||||
# Feed price to sub-strategies
|
||||
if price > 0:
|
||||
self._obi.compute_signal(price=price)
|
||||
self._as_mm.compute_signal(price=price)
|
||||
self._hurst.compute_signal(price=price)
|
||||
|
||||
if self._in_trade and self._check_exit(price):
|
||||
return self._last_signal
|
||||
|
||||
self._bars_since_trade += 1
|
||||
if self._bars_since_trade < self._cooldown_bars:
|
||||
return None
|
||||
|
||||
return self._compute_ensemble()
|
||||
|
||||
# ── Order ───────────────────────────────────────────────────
|
||||
|
||||
def handle_signal(self, signal: dict):
|
||||
side_str = signal.get("signal", "")
|
||||
if "BUY" in side_str:
|
||||
self._submit_order(OrderSide.BUY)
|
||||
elif "SELL" in side_str:
|
||||
self._submit_order(OrderSide.SELL)
|
||||
@@ -0,0 +1,168 @@
|
||||
"""
|
||||
Grid Market Making — structural spread capture without directional signal.
|
||||
|
||||
Places a symmetric grid of limit orders above and below the current mid-price.
|
||||
When a buy fills, a sell is immediately placed one grid level above. When a sell
|
||||
fills, a buy is placed one grid level below. Captures the spread repeatedly
|
||||
during ranging / oscillating markets.
|
||||
|
||||
Architecture:
|
||||
- Backtest: simulate fills from candle high/low ranges
|
||||
- Live/Paper: submit real POST-ONLY limit orders and manage order lifecycle
|
||||
|
||||
Grid params:
|
||||
- grid_levels: number of levels on each side (default 10)
|
||||
- grid_spacing_pct: spacing between levels as % of price (default 0.1%)
|
||||
- order_size: fixed size per grid level (default 0.001 BTC)
|
||||
- rebalance_every: recenter grid every N bars (default 20)
|
||||
- maker_fee: fee for limit orders
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from collections import deque
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
|
||||
from nautilus_trader.model.data import Bar
|
||||
from nautilus_trader.model.enums import OrderSide
|
||||
|
||||
from framework.base_strategy import BaseHlStrategy
|
||||
from framework.config import StrategyConfig
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class GridMMNT(BaseHlStrategy):
|
||||
"""Grid market making — places symmetrical buy/sell grid around mid-price."""
|
||||
|
||||
def __init__(self, config: StrategyConfig):
|
||||
super().__init__(config)
|
||||
|
||||
self._grid_levels = config.params.get("grid_levels", 10)
|
||||
self._grid_spacing_pct = config.params.get("grid_spacing_pct", 0.001) # 0.1%
|
||||
self._rebalance_every = config.params.get("rebalance_every", 20)
|
||||
self._maker_fee = config.maker_fee
|
||||
|
||||
# Virtual order book: {price: {"side": "BUY"/"SELL", "size": float, "filled": bool}}
|
||||
self._grid: dict[float, dict] = {}
|
||||
self._fills: list[dict] = []
|
||||
self._inventory: float = 0.0
|
||||
self._cumulative_pnl: float = 0.0
|
||||
self._bar_count: int = 0
|
||||
self._last_mid: float = 0.0
|
||||
|
||||
# ── Core logic ─────────────────────────────────────────────
|
||||
|
||||
def on_bar(self, bar: Bar):
|
||||
self._bar_count += 1
|
||||
mid = float(bar.close)
|
||||
high = float(bar.high)
|
||||
low = float(bar.low)
|
||||
|
||||
# Initialise or rebalance grid
|
||||
if not self._grid or self._bar_count % self._rebalance_every == 0:
|
||||
self._build_grid(mid)
|
||||
|
||||
# Check fills: compare candle range against grid levels
|
||||
filled_buys = []
|
||||
filled_sells = []
|
||||
|
||||
for price, order in self._grid.items():
|
||||
if order["filled"]:
|
||||
continue
|
||||
if order["side"] == "BUY" and low <= price:
|
||||
order["filled"] = True
|
||||
filled_buys.append((price, order))
|
||||
elif order["side"] == "SELL" and high >= price:
|
||||
order["filled"] = True
|
||||
filled_sells.append((price, order))
|
||||
|
||||
# Process fills
|
||||
for px, order in filled_buys:
|
||||
self._inventory += order["size"]
|
||||
self._cumulative_pnl -= order["size"] * px * self._maker_fee
|
||||
# Place matching sell one grid level up
|
||||
sell_px = px * (1 + self._grid_spacing_pct)
|
||||
self._grid[sell_px] = {"side": "SELL", "size": order["size"], "filled": False}
|
||||
self._fills.append({
|
||||
"side": "BUY", "price": round(px, 1), "size": order["size"],
|
||||
"fee": round(order["size"] * px * self._maker_fee, 6),
|
||||
"bar": self._bar_count,
|
||||
})
|
||||
|
||||
for px, order in filled_sells:
|
||||
# Profit = spread capture minus fees
|
||||
spread_pnl = order["size"] * px * self._grid_spacing_pct
|
||||
fee = order["size"] * px * self._maker_fee
|
||||
self._inventory -= order["size"]
|
||||
self._cumulative_pnl += spread_pnl - fee
|
||||
# Place matching buy one grid level down
|
||||
buy_px = px * (1 - self._grid_spacing_pct)
|
||||
self._grid[buy_px] = {"side": "BUY", "size": order["size"], "filled": False}
|
||||
self._fills.append({
|
||||
"side": "SELL", "price": round(px, 1), "size": order["size"],
|
||||
"pnl": round(spread_pnl - fee, 6), "bar": self._bar_count,
|
||||
})
|
||||
|
||||
self._last_mid = mid
|
||||
|
||||
def _build_grid(self, mid: float):
|
||||
"""Rebuild grid from scratch around current mid price."""
|
||||
self._grid.clear()
|
||||
size = self._cfg.order_size
|
||||
spacing = self._grid_spacing_pct
|
||||
|
||||
for i in range(1, self._grid_levels + 1):
|
||||
buy_px = mid * (1 - i * spacing)
|
||||
sell_px = mid * (1 + i * spacing)
|
||||
self._grid[round(buy_px, 6)] = {"side": "BUY", "size": size, "filled": False}
|
||||
self._grid[round(sell_px, 6)] = {"side": "SELL", "size": size, "filled": False}
|
||||
|
||||
# ── Signal for paper trader / live ─────────────────────────
|
||||
|
||||
def compute_signal(self, price: float | None = None,
|
||||
orderbook: dict | None = None) -> dict | None:
|
||||
"""Return grid quotes for paper trader / deploy orchestrator.
|
||||
|
||||
Returns the full grid of bid/ask prices for the execution layer
|
||||
to submit as limit orders.
|
||||
"""
|
||||
if price is None or price <= 0:
|
||||
return None
|
||||
|
||||
mid = price
|
||||
self._build_grid(mid)
|
||||
|
||||
bids = [(p, o["size"]) for p, o in sorted(self._grid.items(), reverse=True)
|
||||
if o["side"] == "BUY"]
|
||||
asks = [(p, o["size"]) for p, o in sorted(self._grid.items())
|
||||
if o["side"] == "SELL"]
|
||||
|
||||
return {
|
||||
"signal": "GRID",
|
||||
"strength": 1.0,
|
||||
"bids": bids[:5],
|
||||
"asks": asks[:5],
|
||||
"levels": self._grid_levels,
|
||||
"spacing_pct": self._grid_spacing_pct * 100,
|
||||
}
|
||||
|
||||
# ── Metrics ─────────────────────────────────────────────────
|
||||
|
||||
@property
|
||||
def pnl(self) -> float:
|
||||
return self._cumulative_pnl
|
||||
|
||||
@property
|
||||
def total_fills(self) -> int:
|
||||
return len(self._fills)
|
||||
|
||||
@property
|
||||
def inventory(self) -> float:
|
||||
return self._inventory
|
||||
|
||||
def handle_signal(self, signal: dict):
|
||||
"""Grid MM doesn't use single-side signals — handled by on_bar directly."""
|
||||
pass
|
||||
@@ -0,0 +1,212 @@
|
||||
"""
|
||||
Iceberg Detection NautilusTrader strategy.
|
||||
|
||||
Detects whale TWAP/iceberg accumulation by tracking volume spikes and
|
||||
consecutive same-direction large orders.
|
||||
|
||||
Dual-mode:
|
||||
- Backtest: volume spike proxy from candles (volume > avg * multiplier
|
||||
for >= min_consecutive bars in same direction)
|
||||
- Live/Paper: real L2 orderbook wall detection (single level > avg * 3
|
||||
persisting for >= 3 updates)
|
||||
|
||||
Entry: consecutive same-direction spikes/walls → follow smart money
|
||||
Exit: spike count drops below 2 OR trend reverses OR 2% stop-loss
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from collections import deque
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
|
||||
from nautilus_trader.model.data import Bar
|
||||
from nautilus_trader.model.enums import OrderSide
|
||||
|
||||
from framework.base_strategy import BaseHlStrategy
|
||||
from framework.config import StrategyConfig
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class IcebergNT(BaseHlStrategy):
|
||||
"""Iceberg/whale accumulation detection — follows smart money flow."""
|
||||
|
||||
def __init__(self, config: StrategyConfig):
|
||||
super().__init__(config)
|
||||
|
||||
self._vol_lookback = config.params.get("vol_lookback", 40)
|
||||
self._vol_spike_mult = config.params.get("vol_spike_mult", 2.5)
|
||||
self._min_consecutive = config.params.get("min_consecutive", 3)
|
||||
self._max_bars_held = config.params.get("max_bars_held", 8)
|
||||
self._stop_loss_pct = config.params.get("stop_loss_pct", 0.02)
|
||||
self._cooldown_bars = config.params.get("cooldown_bars", 4)
|
||||
|
||||
# Volume tracking
|
||||
self._volumes: deque[float] = deque(maxlen=self._vol_lookback)
|
||||
self._spike_count: int = 0
|
||||
self._prev_spike_dir: str | None = None
|
||||
|
||||
# State
|
||||
self._bars_since_trade = self._cooldown_bars
|
||||
self._bars_held: int = 0
|
||||
self._in_trade = False
|
||||
self._trade_direction: str | None = None
|
||||
self._entry_price: float = 0.0
|
||||
|
||||
# ── Candle mode (backtest) ──────────────────────────────────
|
||||
|
||||
def on_bar(self, bar: Bar):
|
||||
price = float(bar.close)
|
||||
volume = float(bar.volume) if hasattr(bar, 'volume') else 1.0
|
||||
self._prices.append(price)
|
||||
self._volumes.append(volume)
|
||||
|
||||
self._bars_since_trade += 1
|
||||
|
||||
# Exit check
|
||||
if self._in_trade:
|
||||
self._bars_held += 1
|
||||
if self._check_exit(price):
|
||||
return
|
||||
return
|
||||
|
||||
if self._bars_since_trade < self._cooldown_bars:
|
||||
return
|
||||
|
||||
signal = self._detect_iceberg(price, volume)
|
||||
if signal:
|
||||
self._last_signal = signal
|
||||
self.handle_signal(signal)
|
||||
|
||||
def _detect_iceberg(self, price: float, volume: float) -> dict | None:
|
||||
if len(self._volumes) < self._vol_lookback:
|
||||
return None
|
||||
|
||||
avg_vol = np.mean(self._volumes)
|
||||
if avg_vol <= 0:
|
||||
return None
|
||||
|
||||
is_spike = volume > avg_vol * self._vol_spike_mult
|
||||
if not is_spike:
|
||||
self._spike_count = 0
|
||||
self._prev_spike_dir = None
|
||||
return None
|
||||
|
||||
# Determine direction: buy if close > previous close (price going up)
|
||||
if len(self._prices) < 2:
|
||||
return None
|
||||
is_buy = self._prices[-1] > self._prices[-2]
|
||||
spike_dir = "buy" if is_buy else "sell"
|
||||
|
||||
# Track consecutive same-direction spikes
|
||||
if spike_dir == self._prev_spike_dir:
|
||||
self._spike_count += 1
|
||||
else:
|
||||
self._spike_count = 1
|
||||
self._prev_spike_dir = spike_dir
|
||||
|
||||
if self._spike_count >= self._min_consecutive:
|
||||
self._in_trade = True
|
||||
self._trade_direction = "long" if spike_dir == "buy" else "short"
|
||||
self._entry_price = price
|
||||
self._bars_since_trade = 0
|
||||
self._bars_held = 0
|
||||
self._spike_count = 0
|
||||
|
||||
return {
|
||||
"signal": "BUY" if spike_dir == "buy" else "SELL",
|
||||
"strength": min(1.0, self._spike_count / self._min_consecutive),
|
||||
"vol_ratio": round(volume / avg_vol, 1),
|
||||
"spikes": self._spike_count,
|
||||
"reason": f"iceberg_{spike_dir}",
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
# ── L2 mode (live/paper) ────────────────────────────────────
|
||||
|
||||
def compute_signal(self, price: float | None = None,
|
||||
orderbook: dict | None = None) -> dict | None:
|
||||
"""Entry point for paper trader / deploy orchestrator.
|
||||
|
||||
If orderbook provided, use L2 wall detection.
|
||||
Otherwise fall back to candle proxy.
|
||||
"""
|
||||
if orderbook is not None and price is not None:
|
||||
return self._detect_l2_walls(orderbook, price)
|
||||
|
||||
if price is None:
|
||||
return None
|
||||
return self._detect_iceberg(price, 1.0)
|
||||
|
||||
def _detect_l2_walls(self, orderbook: dict, price: float) -> dict | None:
|
||||
"""Detect walls in real L2 orderbook."""
|
||||
bids = orderbook.get("bids", [])
|
||||
asks = orderbook.get("asks", [])
|
||||
|
||||
# Find largest single level size
|
||||
all_sizes = [b[1] for b in bids] + [a[1] for a in asks]
|
||||
if not all_sizes:
|
||||
return None
|
||||
avg_size = np.mean(all_sizes)
|
||||
|
||||
# Check for bid wall (single level > avg * 3)
|
||||
bid_wall = False
|
||||
ask_wall = False
|
||||
for px, sz in bids:
|
||||
if sz > avg_size * 3:
|
||||
bid_wall = True
|
||||
break
|
||||
for px, sz in asks:
|
||||
if sz > avg_size * 3:
|
||||
ask_wall = True
|
||||
break
|
||||
|
||||
if bid_wall and not ask_wall:
|
||||
return {"signal": "BUY", "strength": 0.8, "reason": "l2_bid_wall"}
|
||||
elif ask_wall and not bid_wall:
|
||||
return {"signal": "SELL", "strength": 0.8, "reason": "l2_ask_wall"}
|
||||
return None
|
||||
|
||||
# ── Exit logic ──────────────────────────────────────────────
|
||||
|
||||
def _check_exit(self, current_price: float) -> bool:
|
||||
if not self._in_trade or self._entry_price <= 0:
|
||||
return False
|
||||
|
||||
change_pct = (current_price - self._entry_price) / self._entry_price
|
||||
pnl_pct = change_pct if self._trade_direction == "long" else -change_pct
|
||||
|
||||
exit_reason = None
|
||||
|
||||
if pnl_pct <= -self._stop_loss_pct:
|
||||
exit_reason = "stop_loss"
|
||||
elif self._bars_held >= self._max_bars_held:
|
||||
exit_reason = "time_exit"
|
||||
elif self._spike_count < 2:
|
||||
exit_reason = "spikes_faded"
|
||||
|
||||
if exit_reason is None:
|
||||
return False
|
||||
|
||||
exit_side = "SELL" if self._trade_direction == "long" else "BUY"
|
||||
self._last_signal = {
|
||||
"signal": exit_side,
|
||||
"strength": abs(pnl_pct) / self._stop_loss_pct,
|
||||
"pnl_pct": round(pnl_pct * 100, 2),
|
||||
"bars_held": self._bars_held,
|
||||
"reason": exit_reason,
|
||||
}
|
||||
self._in_trade = False
|
||||
self._trade_direction = None
|
||||
self.handle_signal(self._last_signal)
|
||||
return True
|
||||
|
||||
def handle_signal(self, signal: dict):
|
||||
side_str = signal.get("signal", "")
|
||||
if "BUY" in side_str:
|
||||
self._submit_order(OrderSide.BUY)
|
||||
elif "SELL" in side_str:
|
||||
self._submit_order(OrderSide.SELL)
|
||||
Reference in New Issue
Block a user