0446443d36
feat: creative alpha models + portfolio layer targeting Sharpe > 1.5
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New strategies:
- Cross-Sectional Momentum: long top-N, short bottom-N across HL universe
- Spot-Perp Basis Arbitrage: delta-neutral spot vs perp price gap trading
- Regime-Switching Ensemble: dynamically allocates strategies by market regime
- Portfolio Construction: risk parity, vol targeting, correlation penalty
Infrastructure:
- DuckDBDataProvider: real tick/candle data for backtests (replaces synthetic)
- Walk-Forward Validation: systematic IS/OOS across all 12 strategies
- 3 Jupyter research notebooks (EDA, strategy research, portfolio)
Pipeline integration:
- deploy.py registry, sweep_runner, vbt_runner all updated
- fee_tiers support for new strategies
- All modules syntax-validated and import-tested
2026-08-12 12:26:29 +08:00
20ee340cef
feat: VBT visualization + validation pipeline, HFT tick viz, DuckDB loader
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Track 1 — VBT Candle-Frequency Pipeline:
- backtests/vbt_validator.py: VBTValidator with 11 checks — timestamp monotonicity,
duplicates, NaN, data gaps, lookahead bias, signal alignment, density,
coincident entry/exit, min trade count, fee application, benchmark comparison.
ValidationReport dataclass with errors/warnings/stats. Validates VBT results
or raw signal arrays.
- backtests/vbt_viz.py: VBTVisualizer with 10+ Plotly chart methods — equity
curve with benchmark, drawdown, rolling Sharpe/Sortino/vol, trade markers,
returns distribution with normal fit, monthly PnL heatmap, gross vs net,
holding periods, parameter sensitivity heatmaps, dashboard compositor,
HTML save (self-contained, CDN Plotly). All methods handle empty/null inputs.
- backtests/vbt_report.py: Markdown + HTML report generator — structured
sections for implementation summary, performance metrics, cost analysis,
validation results, signal analysis, known limitations, next steps.
batch_report() for mass report generation from results directory.
- backtests/vbt_runner.py: Added run_benchmark() (buy-and-hold VBT portfolio),
validate() (integrated VBTValidator), run_with_report() (fetch→validate→
backtest→visualize→save in one call).
Track 2 — HFT Tick Pipeline:
- backtests/tick_viz.py: 9-panel HFT dashboard — price+trade markers,
spread dynamics, top-of-book depth, microprice vs mid, OBI/OFI panel,
VPIN toxicity with thresholds, event timeline (PnL from tick_runner),
markout curves at 6 horizons. Parquet→pandas→Plotly pipeline.
Dark-themed HTML output for microstructure review.
- data/duckdb_load.py: Parquet→DuckDB loader — creates l2_snapshots,
trades, funding tables with schema. Pre-computed 1s rollup views for
microprice, OFI, trade imbalance. Markout queries directly in SQL.
Incremental loading with load_state tracking.
CLI Integration:
- cli.py: Added 'report' (full VBT report), 'validate' (check existing
results), 'hft' (tick dashboard generation) commands. Fixed argparse
help string escaping.
355 tests passing (34 new).
2026-08-11 12:22:11 +08:00
7517163142
feat: configurable grid params + auto walk-forward optimizer
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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.
2026-08-10 16:47:09 +08:00
543537e33f
feat: quant validation framework — DSR, PSR, Haircut, regimes, walk-forward
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Three-module quant framework replacing 'sort by Sharpe' with proper
statistical validation:
quant/significance.py (15 tests):
- deflated_sharpe_ratio(): adjusts for N trials (Harvey & Liu 2015)
- probabilistic_sharpe_ratio(): P(True SR > benchmark) given T, skew, kurt
- sharpe_haircut(): expected OOS Sharpe after selection bias deflation
- QuantVerdict: DEPLOY / SIMULATE / DISCARD with 5-point scoring
- validate_strategy(): one-shot validation function
quant/regimes.py (8 tests):
- classify_regime(): trending_up/down, ranging, volatile
- RegimeClassifier: stateful rolling-window classifier
- conditional_performance(): per-regime trade statistics
quant/walkforward.py (5 tests):
- WalkForwardRunner: sequential IS/OOS window optimization
- WFWindow/WFReport: structured walk-forward results
- consistency score, performance decay, concatenated OOS equity
- significance_report() integration
Walk-forward results (real HL data with date-sliced windows):
grid_mm 1h: 2/4 pos, OOS S=-0.45, 74t, haircut=-22.66 → DISCARD
momentum 4h: 2/4 pos, OOS S=-1.47, 116t, haircut=-45.35 → DISCARD
composite_mm 1h: 2/4 pos, OOS S=+2.97, 6t, haircut=+43.25 → SIMULATE
28 tests total
2026-08-10 16:20:56 +08:00
745174f0e6
fix: iceberg strategy — bool dtype + consecutive spike detection
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Root cause: iceberg signal generator produced object-dtype entries
that crashed VectorBT's numba JIT compiler with 'non-precise type
array(pyobject, 1d, C)'. All 28 sweep combos returned 0 trades.
Fixes:
- iceberg: lower vol spike threshold (1.3→1.15), require 2/3
consecutive same-direction spikes (not just single bar)
- exit when spike subsides (not arbitrary 5-bar hold)
- .astype(bool) on all entries/exits before returning from
_generate_signals, preventing numba JIT errors
Results (36/36 succeeded):
iceberg 1d 2000b BTC S=0.18 85t ret=9.38% (best)
iceberg 15m 100b BTC S=-36.34 4t ret=-0.18% (worst)
Consistently negative Sharpe except 1d interval —
volume-spike following loses on sub-daily timescales
2026-08-07 16:27:06 +08:00
0162e83138
feat: Hyperliquid fee schedule — all tiers, staking, maker rebates
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config/fee_tiers.py — complete rewrite:
- 7 perps fee tiers (T0-T6) matching HL docs:
T0: 0.045/0.015% → T6: 0.024/0.000%
- 7 spot fee tiers (T0-T6):
T0: 0.070/0.040% → T6: 0.025/0.000%
- 7 staking tiers (none → diamond):
multiplier 1.00 → 0.60 (40% discount)
- 3 maker rebate tiers (>0.5%, >1.5%, >3% maker ratio)
extra -0.001% to -0.003% on positive maker rates
- compute_trade_fees() — per-trade fee breakdown
- fee_tier_from_volume(), staking_tier_from_hype()
- STRATEGY_FEE_MODELS: maker/taker classification per strategy
backtests/vbt_runner.py:
- Accept vip_tier, staking_tier, maker_rebate_tier at init
- Auto-detect fee model per strategy (maker vs taker)
- compute_trade_fees() for per-trade fee calculation
- Include fee_info in result JSON
dashboard/server.py:
- /api/vbt/run accepts fee_tier/aking_tier/maker_rebate params
- Trade normalization uses proper HL fee schedule per strategy
- /api/vbt/result/{filename}/recalc — recalc trades with new tiers
- /api/vbt/fee_tiers — get full fee schedule as JSON
dashboard/static/vbt.html:
- Fee tier selector (T0-T6) + staking tier selector
- Auto-recalculate on tier change when a result is selected
- Fee rate shown in trade log header (e.g. 0.045%)
2026-08-07 15:44:43 +08:00
9d817ac2fa
feat: VBT trade log — show asset, entry/exit prices, Hyperliquid fees
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Backend (vbt_runner.py):
- Add asset (BTC/ETH) to each trade record
- Compute per-trade fee using HL taker rate (0.05%)
entry_fee = size * entry_px * fee_rate
exit_fee = size * exit_px * fee_rate
- Add pnl_gross (before fees) and pnl_net (after fees)
- Add fee_rate field for transparency
Server (server.py):
- Normalize old backtest trades: add missing asset, fee,
pnl_net, pnl_gross fields
- Holyliquid default fee rate: 0.05% taker
Frontend (vbt.html):
- Trade log table now shows:
Time | Side + Asset | Size | Entry | Exit | Fee | PnL (net) | Duration
- Asset shown as inline badge in Side column
- Fee column with explicit USD amount
- PnL now explicitly labeled 'net' (after fees)
- Fallback to old 'pnl' field for legacy backtest files
2026-08-07 15:35:33 +08:00
887a33f278
feat: trade log table, strategy params panel, B+W color scheme
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Dashboard:
- Trade log table: all trades with time, side, size, entry/exit price, PnL, duration
in scrollable panel below charts
- Strategy params panel: displays all coefficients (z_entry, gamma, obi_entry,
grid_levels, etc.) for the selected strategy
- Color scheme: professional black/white
• positive: #03A9F4 (light blue)
• negative: #FF5252 (red)
• neutral: #777 (gray)
• backgrounds: #0a0a0a / #111 / #181818
• borders: #222 / #333
VBT runner:
- _extract_metrics now captures trades from pf.trades.records_readable
(Avg Entry Price, Avg Exit Price, PnL, Return, Duration, Direction)
- _strategy_params() returns key coefficients per strategy type
- _empty_result includes empty trades/params
New vbt_server.py: minimal standalone dashboard (no live trading machinery,
no memory guard, no broadcast loop) — avoids crashing issues
2026-08-07 12:53:52 +08:00
3606e7f92e
feat: proper Grid MM, Composite MM, Hurst/VPIN, Iceberg, A-S strategies
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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)
2026-08-07 11:42:32 +08:00
37da46a016
feat: proper Order Book Imbalance strategy for BTC-USD on HL
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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
2026-08-07 10:50:43 +08:00
f5ffe4baee
feat: NautilusTrader + VectorBT unified framework for Hyperliquid
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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.
2026-08-06 17:23:49 +08:00
ramseshk