 ramseshk
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3606e7f92e
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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)
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2026-08-07 11:42:32 +08:00 |
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ramseshk
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37da46a016
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feat: proper Order Book Imbalance strategy for BTC-USD on HL
strategies/nt/obi_nt.py:
- Dual-mode OBI: candle proxy (backtest) + real L2 orderbook (live)
- Volume-based imbalance: buy_vol / (buy_vol + sell_vol) over rolling window
- Entry when |imbalance| > 0.35, exit on reversion < 0.10
- Stop-loss 2%, take-profit 0.5%, cooldown 3 bars
- compute_signal(price, orderbook=None) for paper trader integration
backtests/vbt_runner.py:
- Replaced placeholder z-score with proper volume-based OBI
- Buy vol = volume where close > open, sell vol = volume where close < open
- Rolling window imbalance computation
- Parameter sweep support with 12 combos tested
Registered across: deploy.py, nt_runner.py, dashboard, strategies/nt/__init__
Verified:
- VectorBT OBI backtest: 15 trades, -7.2% on default (window=20)
- Param sweep best: w=30 t=0.35 → sharpe -0.82, 49% win, 23% DD
- Real L2 orderbook signal: BUY obi=0.880 (bids 88% of depth)
- NT backtest engine: 201 bars, 8 days, 236ms
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2026-08-07 10:50:43 +08:00 |
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ramseshk
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f5ffe4baee
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feat: NautilusTrader + VectorBT unified framework for Hyperliquid
Add complete framework for testing and deploying quant strategies:
Framework (framework/):
- HyperliquidInstrumentCatalog: loads perps as NT CryptoPerpetual
- HyperliquidDataProvider: real candle/orderbook/mark-price data
- HyperliquidExecutionProvider: live + PaperExecutionProvider: simulated
- BaseHlStrategy: shared NT strategy lifecycle with signal library
- StrategyConfig: YAML-based parameter management
- DeployOrchestrator: CLI for backtest -> paper -> live pipeline
Backtesting (backtests/):
- VBTBacktestRunner: VectorBT vectorized backtests on real HL candles
- NTBacktestRunner: NautilusTrader event-driven backtest engine
NT Strategy ports (strategies/nt/):
- PairsTradingNT: BTC/ETH ratio Z-score mean reversion
- HurstVPINNT: Hurst exponent regime + VPIN flow imbalance
- ASMarketMakingNT: Avellaneda-Stoikov stochastic control MM
E2E verified: real HL candles fetch, VectorBT backtest (Sharpe 5.2
on Hurst/VPIN), instrument catalog, deploy CLI --list, strategy signals.
Existing live/node.py and paper_trader.py unchanged.
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2026-08-06 17:23:49 +08:00 |
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