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Author SHA1 Message Date
ramseshk cdc7a01986 feat: queue simulation validation — VBT vs realistic event-driven PnL comparison 
Track 4 — quant/sim_validate.py:
  SimulationValidator: runs VBT backtest, then replays signals through
  Phase 3 event-driven simulator with queue position, partial fills,
  adverse selection, and cancel latency.

  Compares VBT (optimistic candle fill) vs simulator (realistic) PnL.
  BatchSimValidator: runs multiple param combos through the pipeline.

Grid MM simulation results (all DISCARD):
  VBT S=+0.78, ret=+1.5% → Sim S=0.00, ret=0.0%, 100% adverse
  VBT S=-0.03, ret=-0.4% → Sim S=0.00, ret=0.0%, 100% adverse
  VBT S=-2.74, ret=-6.1% → Sim S=0.00, ret=0.0%, 100% adverse

  Grid fills happen during momentum (price crossing levels),
  not mean reversion. Adverse selection rate approaches 100%.
  VBT candle fills are fundamentally optimistic for grid strategies.

269 tests passing.
 2026-08-10 16:48:31 +08:00
ramseshk 7517163142 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.
 2026-08-10 16:47:09 +08:00
ramseshk ad4036713e feat: recent-window backtests (week/month/year) — 207 clean results 
Archived 639 old full-history results. Re-ran sweep on time-windowed
bar limits matching practical trading horizons:

  Last week bars:  1h=168, 4h=42,  1d=7
  Last month bars: 1h=720, 4h=180, 1d=30
  Last year bars:  1h=5000, 4h=2190, 1d=365

270/270 sweep succeeded, 63 zero-trade files archived.
207 results with real trades remain.

Key findings on RECENT (year) data only:

  grid_mm:    S=13.8 (1h), S=9.3 (4h), S=8.1 (1d)
  as_mm:      S=14.8 (1h), S=8.0 (4h), S=3.3 (1d)
  composite:  S=3.2 (1h), S=2.3 (4h), S=0.7 (1d)
  mean_rev:   1d only viable (S=3.5)

  Directional strategies (momentum, obi, iceberg, hurst_vpin)
  all show near-zero or negative Sharpe on recent data.

  Market-making dominates in sideways crypto markets.

Added --window flag to sweep_runner for week/month/year presets
 2026-08-10 16:35:25 +08:00
ramseshk 543537e33f feat: quant validation framework — DSR, PSR, Haircut, regimes, walk-forward 
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
ramseshk 268fe606fa fix: clear stale __pycache__ causing 'datetime is not defined' error 2026-08-10 14:03:56 +08:00
ramseshk e71b47ac56 refactor: VBT sidebar — cleaner UX with grouped sections + collapsible fee tiers 
Sidebar reorganized into clear sections:
  [FILTERS] — Strategy + Asset + Interval + Sort
  ▶ Fee Tiers — collapsible (hidden by default), shows summary
    T0 + staking tier name when collapsed
  [NEW BACKTEST] — compact: Strategy (short names) + Interval + Limit
    Row 2: Coin + ▶ Run button (side by side) + ↻ Refresh
  [RESULTS] — count header + list + pagination

Removed:
  - Full-width run strategy row (now inline with interval/limit)
  - Duplicate fee tier labels in select options
  - Separate run/refresh button row (now same row as coin)

Added:
  - toggleFeeTiers() — expand/collapse fee settings
  - updateFeeSummary() — shows current tier in collapsed label
  - Fee changes auto-recalculate (no manual onchange needed)
  - Section headers: small uppercase labels
 2026-08-10 12:58:37 +08:00
ramseshk 0486e8c93f feat: VBT overview + light mode + /api/vbt/summary 
dashboard/static/vbt.html — complete rewrite:
  - CSS variables for theming (--bg, --text, --blue, etc.)
  - Light mode toggle (☀/☾) persisted in localStorage
  - Plotly charts update colors on theme switch
  - Overview panel on load: strategy cards + heatmap
  - Strategy cards: best Sharpe, best return, top combo,
    +Sharpe %, run count. Click to filter.
  - Heatmap: color-coded strategy×interval table
    (green>2, green>0.5, yellow>0, orange>-0.5, red)
  - Back-to-overview button on detail view
  - Default sort: Sharpe (was date)

dashboard/server.py:
  - New /api/vbt/summary endpoint:
    Per-strategy best (Sharpe, Return, Calmar, trades,
    positive-Sharpe %, top combo)
    Heatmap: strategy×interval best Sharpe matrix

Tests: 241 passing (excl. Hawkes)
 2026-08-10 12:38:31 +08:00
ramseshk b7c7fbb0af feat: VBT dashboard overview — strategy cards + heatmap + /api/vbt/summary 
dashboard/server.py — new /api/vbt/summary endpoint:
  - Aggregated per-strategy best stats (Sharpe, Return, Calmar, trades,
    positive-Sharpe %, top combo details)
  - Heatmap: strategy × interval matrix with best Sharpe per cell

dashboard/static/vbt.html — overview panel replaces empty state:
  - Strategy cards grid: best Sharpe, best return, top combo,
    positive-Sharpe %, run count. Click to filter sidebar.
  - Heatmap: color-coded strategy × interval table (green=good,
    red=bad). Hover for tooltip, click to load result.
  - Back-to-overview button when viewing a strategy detail
  - Overview re-shown on page load, auto-loads from /api/vbt/summary

Dashboard opens to overview → select strategy → drill into details
 2026-08-10 12:03:49 +08:00
ramseshk 140b0cc360 feat: full-information live terminal — all 10 modules with coefficients 
live/monitor_service.py — rewritten state() includes all 10:
  - Hawkes: 3x3 alpha excitation matrix, mu baseline per type, beta decay
  - Dealer GEX: net GEX (), pin levels, direction signal
  - Tick Regime: current/next tick size, boundary price, bars-to-cross
  - Triangular Arb: venue count, opportunities, best spread bps
  - Sequencer: stale-state detection, P50/P99 latency, event count
  - Full: prices, microstructure, HLP, whipsaw, liq, term, spoof

live.html — complete rewrite: Bloomberg-terminal 4-column grid
  - Panel 1: Prices (mid, mark, oracle, premium, spread, funding)
  - Panel 2: Microstructure (OBI, best bid/ask, toxicity bar, branching ratio, sequencer latency P50/P99)
  - Panel 3: Composite Signals (HLP, Whipsaw w/countdown, Term Structure w/z-score, GEX w/signal, Tick Regime, Spoof probability bar)
  - Panel 4: Dealer GEX (net GEX bar, pin levels, direction)
  - Full-width: HLP Vault (total delta, assets, toxicity, per-coin signals)
  - Wide: Hawkes Coefficients (3x3 alpha matrix, mu per type, beta)
  - Liq Waterfall + Tri Arb + Sequencer + Tick Regime
  - Term Structure + Funding Whipsaw with countdown
 2026-08-10 10:54:54 +08:00
ramseshk e0be9f4d40 feat: advanced microstructure — sequencer latency, dealer GEX, tick regime, triangular arb 
4 new modules with 20 tests:

#22 Sequencer Latency Detector (live/monitors/sequencer_latency.py):
  Detects stale-state windows between WebSocket and REST API.
  - WebSocket vs REST timestamp delta tracking
  - Transport latency percentiles (p50, p99)
  - Liquidation-triggered stale-state detection
  - Stale asset identification for cross-margin arbitrage

#25 Dealer GEX (microstructure/dealer_gex.py):
  Dealer Gamma Exposure estimation via Black-Scholes.
  - Per-strike gamma × OI × spot² GEX computation
  - Pin level detection (strikes where dealers are long gamma)
  - Net GEX aggregation
  - Signal: fade_breakout (long gamma pinning) vs ride_momentum
    (short gamma amplification)
  - nearest_pin() for distance-to-magnet calculation

#30 Tick-Size Regime Exploitation (microstructure/tick_regime.py):
  Detects when asset price approaches tick-size boundaries.
  - Hyperliquid tick schedule (BTC 0.1/0.5, ETH 0.01/0.05, SOL 0.001/0.005)
  - Boundary approach detection with configurable threshold
  - Linear trend estimation for expected bars-to-cross
  - Signal: widen_quotes or tighten_quotes with urgency classification
  - Per-coin state tracking

#33 Triangular Latency Arb (live/monitors/triangular_arb.py):
  Cross-venue A→B→C triangular arbitrage detection.
  - Internal triangular: BTC-USDT → ETH-BTC → ETH-USDT
  - Cross-venue: price discrepancy across slow/fast venues
  - Latency gap detection between venue pairs
  - Implied cross-rate computation vs direct quote
  - Minimum spread threshold gating
 2026-08-10 10:34:56 +08:00
ramseshk 27c096dc9e feat: live monitoring dashboard — Bloomberg-terminal UI for all advanced modules 
live/monitor_service.py (LiveMonitorService):
  Background service running all 6 advanced monitors plus microstructure.
  Polls Hyperliquid REST API every 4s for prices, books, funding.
  Exposes unified state() method for the live dashboard API.

dashboard/server.py:
  Added /api/monitors/status?coin=BTC — combined state of all monitors
  Added /api/monitors/hlp — HLP vault state
  Added /api/monitors/funding — funding whipsaw signal
  Added /api/monitors/spoof — spoof detector summary
  Added /live route serving the live dashboard
  Monitor service auto-starts on first API request

dashboard/static/live.html:
  Professional dark-themed live monitoring dashboard.
  Three-column grid layout with real-time polling:
  - Price panel: mid, mark, oracle, premium, spread, funding
  - Microstructure panel: OBI, VPIN/toxicity bar, bid/ask depths
  - Composite signal panel: HLP signal, funding whipsaw signal,
    term structure signal, spoof probability bar, branching ratio
  - HLP Vault panel: total delta, assets tracked, toxicity %,
    overextended assets, per-coin rebalancing signals
  - Bottom row: Spoof Detector, Liquidation Waterfall,
    Term Structure
  - Coin selector (BTC/ETH) with color-coded signal rows
  - Auto-refreshes every 3s with live indicator

Access: http://localhost:9175/live
 2026-08-07 18:00:48 +08:00
ramseshk 5304534e38 feat: advanced microstructure modules — HLP, Hawkes, Whipsaw, Term Structure, Liq Waterfall, Spoof Detector 
6 new modules with 46 new tests (230 total):

#21 HLP Vault Monitor (live/monitors/hlp_vault.py):
  Tracks Hyperliquid's native protocol market maker at address
  0xfefefe... Queries clearinghouseState + metaAndAssetCtxs.
  - Delta exposure per asset (notional + PnL)
  - Overextension detection (notional exceeds  M threshold)
  - Rebalancing signals: fade_short when HLP too short,
    fade_long when HLP too long (front-run forced rebalancing)
  - Toxicity score: HLP losing money = absorbing informed flow
  - Historical delta tracking

#29 Hawkes Processes (microstructure/hawkes.py):
  Multivariate Hawkes calibrator for limit order book dynamics.
  - MLE calibration via SGD gradient descent on log-likelihood
  - Branching ratio enforcement (alpha/beta < 0.99 for stationarity)
  - Intensity computation λ_i(t) with cross-excitation
  - Activity forecasting (expected event count in horizon)
  - Synthetic event generator (Ogata thinning)
  - Pure functions: hawkes_intensity, hawkes_log_likelihood,
    generate_hawkes_events

#23 Funding Whipsaw Trader (live/strategies/funding_whipsaw.py):
  Premium index decay trading in final 60s of funding epoch.
  - Detects deterministic convergence of premium→0 at settlement
  - Time-scaled position sizing (larger closer to settlement)
  - Auto-close after funding epoch completes
  - Confidence scoring based on premium magnitude

#32 Term Structure Monitor (live/monitors/term_structure.py):
  Perp/quarterly/bi-quarterly futures basis curve trading.
  - Quarterly-perp basis with z-score anomaly detection
  - BiQ-quarterly curve steepness monitoring
  - Fair quarterly price via interest rate parity + funding carry
  - Calendar spread signals: buy_basis, sell_basis,
    curve_steepener, curve_flattener

#24 Liquidation Waterfall (live/monitors/liq_waterfall.py):
  Cross-margin liquidation order prediction.
  - Margin ratio tracking (equity / maintenance margin)
  - Danger/critical level classification
  - Asset liquidation priority: maintenance / book_liquidity ratio
    (least liquid asset relative to margin = dumped first)
  - Strategy output: widen_spreads on target, tighten on rest

#31 Spoof Detector (microstructure/spoof_detector.py):
  Adversarial ML-style spoofing pattern recognition.
  - Rule 1: Large order far from mid, cancelled immediately
  - Rule 2: Cancel right before trade approaches price level
  - Rule 3: Oversized order with no fill within short lifetime
  - Spoof probability (rolling window ratio)
  - Cancel-to-fill ratio monitoring
 2026-08-07 17:52:20 +08:00
ramseshk 31c1fe7fbe chore: sweep all strategies on HFT/intraday/intraweek intervals — 639 clean results 
Archived all old historical/ files (zero trade prices, broken).
Removed 67 zero-trade sweep files.

Fresh sweep: 360/360 succeeded across 9 strategies
  HFT (1m,5m), intraday (15m,1h,4h), intraweek (1d)
  6 bar limits (100-5000), BTC + ETH for pairs

Key results by timeframe:

HFT (1m/5m, 235 files):
  hurst_vpin 1m 200b     S=82.05  1t   +0.60%
  hurst_vpin 1m 100b     S=62.06  1t   +0.22%
  momentum    1m 100b     S=31.96  2t   +0.16%
  NOTE: 1m results are noisy, single-trade Sharpe unreliable

INTRADAY (15m/1h/4h, 303 files):
  hurst_vpin 15m 100b     S=19.38  1t   +0.58%
  grid_mm    1h  100b     S= 8.56  4t   +2.50%
  obi        15m 100b     S= 6.95  2t   +0.29%
  momentum   4h  100b     S= 2.91  3t   +2.70%

INTRAWEEK (1d, 101 files):
  hurst_vpin 1d  100b     S= 1.17  1t   +5.03%
  grid_mm    1d  5000b    S= 0.78  1t   +446%*
  momentum   1d  5000b    S= 0.51  66t  +56%
  mean_rev   1d  5000b    S= 0.69  111t +37%

* grid_mm 1d returns suspiciously high — likely signal artifact

VBT Dashboard: 639 results, paginated, searchable
 2026-08-07 17:34:55 +08:00
ramseshk 2aca789581 fix: NaN win_rate/profit_factor/expectancy crash JSON serialization 
Root cause: VBT produces NaN for win_rate, profit_factor, and
expectancy when all trades have zero PnL (no winners, no losers).
JSON.dumps() rejects NaN/Inf values with 'ValueError: Out of range
float values are not JSON compliant'.

Fixes:
- Add _sanitize_nan(): recursive NaN/Inf → 0.0 for JSON safety
- Apply at end of _normalize_vbt_fields (after field fixes)
- Fix win_rate check: use 'is None' instead of 'not' (0.0 is falsy)
- Previously crashing files now work:
  grid_mm_BTC_4h_100b, hurst_vpin_BTC_1m_100b, etc.
 2026-08-07 16:46:14 +08:00
ramseshk 745174f0e6 fix: iceberg strategy — bool dtype + consecutive spike detection 
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
ramseshk 78a170a42b feat: VBT sweep runner — batch backtest all strategy/interval/limit/coin combos 
backtests/sweep_runner.py:
  - Generates all valid strategy x interval x limit x coin combinations
    (9 strategies, 6 intervals, 6 bar limits, 2 coins = 360 combos)
  - Sequential mode with configurable rate-limiting delay
  - Multi-process parallel mode (ProcessPoolExecutor)
  - Progress logging, ETA, summary stats
  - --dry-run flag to preview without executing
  - CLI: python -m backtests.sweep_runner --workers 2 --delay 1.0

Results from initial sweep (262/360 succeeded):
  Best performers:
    grid_mm 1h 100b    S=5.08  ret=1.45%  4 trades
    composite_mm 4h    S=3.50  ret=1.50%  1 trade
    momentum 4h  200b  S=3.12  ret=5.94%  7 trades
    hurst_vpin 4h 100b S=2.88  ret=1.89%  1 trade

  Regime issues found:
    - 1m interval: universally extreme negative Sharpe
      (VBT can't model sub-minute HFT dynamics)
    - iceberg: generated 0 trades across all 28 runs (signal bug)
    - as_mm: many combos with 'no results' (A-S in low vol)
    - mean_rev: consistent negative across all combos

Archived 67 old broken files, kept 310 clean sweep results
 2026-08-07 16:14:13 +08:00
ramseshk 7d5b05b640 chore: archive 7 broken backtest files with no equity curves 
Archived files (backtests/results/archive/):
  as_mm_BTC_20260806-081121.json  — 506 trades, no EC, no PnL
  as_mm_ETH_20260806-081122.json  — 505 trades, no EC, no PnL
  as_mm_HYPE_20260806-081123.json — 510 trades, no EC, no PnL
  as_mm_VVV_20260806-081123.json  — 512 trades, no EC, no PnL
  hurst_vpin_BTC_20260806-071308.json — 47 trades, no EC
  spx_reversion_1h_20260806-073445.json — 38 trades, no EC
  spx_reversion_30m_20260806-073445.json — 22 trades, no EC

Remaining: 102 files with valid equity curves (720+ points)
and meaningful Sharpe ratios + trade data
 2026-08-07 15:52:04 +08:00
ramseshk 0162e83138 feat: Hyperliquid fee schedule — all tiers, staking, maker rebates 
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
ramseshk 9d817ac2fa feat: VBT trade log — show asset, entry/exit prices, Hyperliquid fees 
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
ramseshk 9ee13a45bb fix: LTTB downsampling crashes on string timestamps + abs→Math.abs 
- _lttb_downsample: equity curve timestamps are ISO strings,
  not numeric. Rewrote to use array indices for triangle area
  instead of trying to multiply string * float.
- Fixed IndexError from reusing 'a' variable incorrectly
- Also fixed earlier: abs(dd) → Math.abs(dd) in vbt.html
 2026-08-07 15:26:42 +08:00
ramseshk 870df57051 fix: dashboard errors — Math.abs, API routing, fetchHistorical guard 
Fixes three bugs in dashboard:

1. vbt.html: bare abs(dd) → Math.abs(dd) — fixes ReferenceError
2. server.py: Add /cv/ routing for Next.js quant dashboard
   - Mount _next static assets at /cv/_next (not /cv/ which eats API routes)
   - Add /cv/api/* routes for backtests/historical, detail, recalc, risk
   - Add /cv/ws WebSocket endpoints for live/paper metrics
   - Add /cv/ catchall for Next.js HTML pages
3. dashboard-next/src/lib/api.ts: add res.ok guard to fetchHistorical()
   — prevents SyntaxError when API returns HTML error pages
4. sim/maker.py: guard observe() against zero mid_price
 2026-08-07 15:19:18 +08:00
ramseshk e58c5951b7 feat: Phase 5 — integration layer (analytics pipeline, production node v2, CLI) + 12 tests 
live/integrator.py (AnalyticsPipeline):
  Real-time pipeline: data → microstructure → signals.
  Accumulates book snapshots + trades, computes OBI, VPIN, microprice,
  spread, depth, trade imbalance, HFT regime, and emits composite
  signal with confidence and breakdown. Per-coin isolation.

live/node_v2.py (ProductionNode):
  Rebuilt production node integrating ALL Phase 1-4 modules:
  - REST data fetching (order book, mark prices, funding rates)
  - AnalyticsPipeline per coin for real-time microstructure signals
  - Treasury for position/capital/PnL/breaker management
  - ToxicityFilter integration via HlMakerPool makers
  - HlMakerPool for per-coin A-S quoting
  - CrossVenueMonitor, FundingBasisMonitor, LiquidationRiskOverlay
  - Paper trading with probabilistic fill simulation
  - Dashboard metrics JSON output (equity, treasury, analytics, maker)
  - Periodic status logging

cli.py (unified CLI):
  Subcommands integrating all modules:
    collect   — Run Hyperliquid data collector to Parquet
    analyze   — Run microstructure analytics on stored data
    simulate  — Run market-making simulator on stored data
    run       — Start production trading node (paper or live)
    backtest  — Run VectorBT backtest

12 integration tests (all pass):
  - AnalyticsPipeline: empty, book, trade, VPIN, emit, regime, isolation
  - ProductionNode: creation, tick cycle (3 ticks), metrics JSON output
  - CLI: import verification

Total test suite: 184 tests, all passing.
 2026-08-07 14:54:47 +08:00
ramseshk 4f66ef36a9 feat: Phase 4 — controlled strategy deployment module + 38 tests 
New live/ sub-modules for production-ready market making:

live/filters/toxicity.py (ToxicityFilter):
  VPIN-based pre-trade filter. Accumulates buy/sell volume, computes
  VPIN via microstructure module, produces quoting decision:
    - allow_quoting: bool
    - size_multiplier: 0.0–1.0 (graduated reduction approaching alarm)
    - granular thresholds (threshold vs alarm) with smooth reduction

live/treasury.py (Treasury):
  Central capital/risk management — single source of truth:
  - Position tracking per coin (opening, closing, average entry)
  - Realized + unrealized PnL computation
  - Pre-trade constraint checks (inventory limits, fee estimates)
  - Circuit breaker (drawdown, trade count, toxic fill rate, API errors)
  - Liquidation distance monitoring
  - Automatic cooldown reset after trip expiry

live/makers/hl_btc_eth.py:
  HlMaker — per-coin market maker integrating:
    - AvellanedaStoikovMaker (Phase 3) for optimal quotes
    - ToxicityFilter for pre-trade gating
    - Treasury for position/risk checks
  HlMakerPool — manages multiple HlMaker instances with shared treasury
    and coordinated observe_all()/quote_all()

live/monitors/cross_venue.py (CrossVenueMonitor):
  Cross-exchange lead-lag detection via cross-correlation at multiple
  lags. Spot premium (basis proxy) computation. Multi-venue summary.

live/monitors/funding_basis.py (FundingBasisMonitor):
  Funding regime classification, momentum detection, carry PnL
  estimation, basis spread analysis. Uses microstructure/funding.py.

live/monitors/liq_risk.py (LiquidationRiskOverlay):
  Per-position liquidation distance monitoring with tiered warnings
  (safe/warning/danger/critical). Recommended position reduction.

38 tests across 4 files (all pass):
  test_live_filters.py (5)
  test_live_maker.py (9)
  test_live_monitors.py (11)
  test_live_treasury.py (13)

Total test suite: 172 tests, all passing.
 2026-08-07 14:47:08 +08:00
ramseshk 639dd4fb6d feat: Phase 3 — event-driven market-making simulator + 53 tests 
New sim/ module — 7 files + init, replays stored L2/trade data
through a realistic market-making simulation:

sim/engine.py (SimulationEngine):
  Event-driven core — processes L2 updates, trades, mark prices
  sequentially. Orchestrates queue model, maker quotes, fill sim,
  constraints, scenarios. Supports periodic re-quoting and
  stale order cancellation.

sim/queue.py (QueueModel):
  Price-time FIFO queue per price level. Tracks where maker orders
  sit in queue. Simulates order eating by aggressor trades.
  fill_probability() — Poisson thinning model for fill odds.

sim/maker.py:
  AvellanedaStoikovMaker — stochastic control quoting with
    aeta, k, tau parameters. Reservation price based on inventory.
    quote() and quote_with_skew() with configurable inventory tilt.
  GridMaker — evenly-spaced grid quoting at N levels.

sim/fills.py:
  FillSimulator — partial fills, adverse selection probability,
    cancel latency (gaussian RTT). FillEvent/CancelEvent tracking.
  adverse_selection_intensity() — measures post-fill price moves.

sim/constraints.py:
  InventoryConstraint — long/short/net/gross position limits.
  FundingConstraint — hourly funding cost estimation.
  FeeSchedule — maker/taker fee calculation.
  LiquidationRisk — liquidation price and safety distance.
  CircuitBreaker — PnL, trade count, toxic rate, slippage trips.
  ConstraintManager — unified pre-trade constraint check.

sim/scenario.py:
  ScenarioEngine — randomized exchange downtimes, latency spikes,
    volatility bursts. State query per sim_time for spread/trade-rate.

sim/reporter.py:
  PnLReporter — component-level PnL breakdown:
    spread_capture, inventory_pnl, fees, funding, adverse_selection.
  SimulationStats — trade counts, fill rates, drawdown, sharpe.
  Equity curve tracking and max drawdown computation.

53 new tests across 4 files (all pass):
  test_sim_queue.py (12) — order placement, FIFO, fills, cancels
  test_sim_maker.py (9) — A-S quotes, inventory skew, grid maker
  test_sim_constraints.py (14) — limits, funding, fees, liquidation, breakers
  test_sim_reporter.py (12) — PnL components, equity curve, stats
  test_sim_engine.py (6) — full engine integration

Total test suite: 134 tests, all passing.
 2026-08-07 14:39:59 +08:00
ramseshk fcfc136384 feat: Phase 2 — microstructure analytics + 81 tests 
New microstructure/ module with pure-function analytics:

microstructure/book.py:
  microprice() — depth-weighted mid price
  mid_price() — simple bid/ask midpoint
  order_book_imbalance() — ranged [-1, 1] volume skew
  depth_imbalance() — imbalance at fixed price distance
  spread_stats() — spread, spread_bps, mid, bid, ask
  depth_resiliency() — bid/ask volume within impact radius
  queue_depletion_prob() — Poisson fill probability at level
  batch_book_stats() — aggregate stats across snapshots

microstructure/trades.py:
  classify_lee_ready() — Lee-Ready aggressor classification
  classify_bulk_lee_ready() — batch classification with mids/bids/asks
  compute_markouts() — forward mid-price change at configurable horizons
  markout_summary() — mean/std/t-stat per side per horizon
  trade_volume_profile() — size bucket distribution
  trade_arrival_rate() — rolling trades/sec with burst detection

microstructure/toxicity.py:
  compute_vpin() — volume-synchronized informed trading probability
  compute_vpin_time_series() — rolling VPIN with alarm threshold
  fill_toxicity() — adverse price movement post-trade
  adverse_selection_ratio() — per-side adverse selection
  liquidation_clustering() — cluster detection in liquidation events

microstructure/funding.py:
  funding_regime() — classify regime (neutral/positive/negative/high)
  funding_predictability() — AR(1) autocorrelation analysis
  funding_carry_pnl() — cumulative carry PnL estimation
  basis_spread() — perp premium over spot (bps)
  basis_convergence_speed() — mean-reversion half-life via AR(1)

microstructure/signals.py:
  composite_signal() — weighted OBI + trade + VPIN + funding signal
  SignalPipeline — stateful pipeline accumulating book/trade updates
  detect_hft_regime() — regime classifier for HFT strategy selection

Bug fixes in Phase 1:
  - data/latency.py: proper linear-interpolation percentiles
  - data/normalizer.py: UTC timezone for naive datetimes
  - data/normalizer.py: detect_sequence_gap returns gap-1 (missing count)
  - microstructure/toxicity.py: consistent vpin_value key in compute_vpin

81 tests across 4 test files (store, normalizer, latency, microstructure)
 2026-08-07 14:34:18 +08:00
ramseshk a7f811eb81 feat: Phase 1 — real-time & historical data system 
New data/ module with:
- data/store.py: Parquet-based raw message storage with background writer
  thread. Messages partitioned by channel/coin/date. Thread-safe queue.
  Supports pyarrow Parquet with zstd compression. Includes read_range()
  helper for replay.

- data/collectors/hyperliquid.py: HL WebSocket + REST collector
  - WebSocket: l2Book (full book reconstruction), trades, allMids (mark prices)
  - REST pollers: funding rates, predicted funding, open interest, liquidations
  - Per-coin OrderBook class with snapshot/update reconstruction
  - Sequence gap detection with per-coin re-snapshot on gap
  - Latency tracking (exchange transport, signal, order, roundtrip)
  - Periodic stats reporter (book stats + latency summary every 60s)
  - CLI entrypoint: python -m data.collectors.hyperliquid --coins BTC ETH

- data/normalizer.py: Timestamp normalization (ms, s, ISO strings from
  HL/Binance/Bybit/OKX/Coinbase/Deribit) + SequenceTracker with gap detection

- data/latency.py: Rolling-window latency metrics (p50/p90/p95/p99) for
  transport, signal computation, order submission, and roundtrip

- Added pyarrow + aiohttp to requirements.txt
 2026-08-07 14:28:21 +08:00
ramseshk b13ce68fef fix: inverted condition in loadResults — new API response format was falling into wrong branch 2026-08-07 14:17:03 +08:00
ramseshk 6889e06a86 feat: VBT dashboard overhaul — pagination, caching, LTTB downsampling, deep links, export, more metrics 
- Merge vbt_server.py into server.py (eliminate duplicated VBT API)
- Add server-side pagination (offset/limit) with metadata (total, has_more)
- Add server-side ?asset= filtering to results endpoint
- Add JSON file caching with 5s TTL to avoid re-parsing on every request
- Add LTTB (Largest-Triangle-Three-Buckets) downsampling for equity curves
- Add pre-computed drawdown curve to result detail response
- Add /api/vbt/result/{filename}/csv endpoint for trade export
- Add Calmar ratio and expectancy to results metadata
- Rebuild vbt.html frontend with:
  - URL hash deep-linking (#filename) for bookmarkable views
  - JSON and CSV export buttons in detail panel
  - More metrics: Calmar, Sortino, Expectancy, End Equity (10 total)
  - Running backtest progress indicator with elapsed seconds
  - Pagination controls (prev/next) with page info
  - Filter/sort changes auto-apply (no manual refresh needed)
  - Better error states with retry buttons
  - Run strategy selector independent of filter
 2026-08-07 14:14:40 +08:00
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{"strategy": "grid_mm", "interval": "4h", "n_bars": 43, "start_equity": 10000.0, "end_equity": 10199.49, "total_return_pct": 1.99, "pnl": 199.49, "sharpe": 7.002, "sortino": 13.846, "max_drawdown_pct": 0.83, "win_rate": 1.0, "profit_factor": Infinity, "expectancy": 154.748, "trades": [{"time": "2026-08-09 04:00:00", "side": "BUY", "asset": "BTC", "size": 0.156902, "entry_px": 63724.66, "exit_px": 64730.21, "pnl_gross": 154.7482, "pnl_net": 151.725, "fee": 3.023224, "fee_rate_pct": 0.015, "return_pct": 1.548, "duration": ""}, {"time": "2026-08-10 08:00:00", "side": "BUY", "asset": "BTC", "size": 0.156254, "entry_px": 64978.91, "exit_px": 65275.0, "pnl_gross": 44.7417, "pnl_net": 41.6888, "fee": 3.052904, "fee_rate_pct": 0.015, "return_pct": 0.441, "duration": ""}], "params": {"grid_levels": 10, "grid_spacing_pct": 0.1, "rebalance_every": 20, "type": "Market Making"}, "fee_info": {"entry_fee": 0.015, "exit_fee": 0.015, "total_fee": 0.03, "taker_rate_pct": 0.045, "maker_rate_pct": 0.015, "effective_rate_pct": 0.015, "vip_tier": 0, "staking_tier": "none", "fee_model": "maker", "tier_name": "Tier 0 (<$5M)", "staking_name": "No Stake"}, "equity_curve": [{"t": "2026-08-03T08:00:00+00:00", "v": 10000.0}, {"t": "2026-08-03T12:00:00+00:00", "v": 9988.51}, {"t": "2026-08-03T16:00:00+00:00", "v": 10015.34}, {"t": "2026-08-03T20:00:00+00:00", "v": 9962.0}, {"t": "2026-08-04T00:00:00+00:00", "v": 10006.71}, {"t": "2026-08-04T04:00:00+00:00", "v": 9988.2}, {"t": "2026-08-04T08:00:00+00:00", "v": 10025.85}, {"t": "2026-08-04T12:00:00+00:00", "v": 10055.98}, {"t": "2026-08-04T16:00:00+00:00", "v": 10073.4}, {"t": "2026-08-04T20:00:00+00:00", "v": 10052.37}, {"t": "2026-08-05T00:00:00+00:00", "v": 10066.33}, {"t": "2026-08-05T04:00:00+00:00", "v": 10063.35}, {"t": "2026-08-05T08:00:00+00:00", "v": 10048.13}, {"t": "2026-08-05T12:00:00+00:00", "v": 10096.77}, {"t": "2026-08-05T16:00:00+00:00", "v": 10166.6}, {"t": "2026-08-05T20:00:00+00:00", "v": 10139.76}, {"t": "2026-08-06T00:00:00+00:00", "v": 10121.88}, {"t": "2026-08-06T04:00:00+00:00", "v": 10164.71}, {"t": "2026-08-06T08:00:00+00:00", "v": 10135.53}, {"t": "2026-08-06T12:00:00+00:00", "v": 10135.53}, {"t": "2026-08-06T16:00:00+00:00", "v": 10106.97}, {"t": "2026-08-06T20:00:00+00:00", "v": 10087.67}, {"t": "2026-08-07T00:00:00+00:00", "v": 10082.34}, {"t": "2026-08-07T04:00:00+00:00", "v": 10086.89}, {"t": "2026-08-07T08:00:00+00:00", "v": 10197.66}, {"t": "2026-08-07T12:00:00+00:00", "v": 10179.15}, {"t": "2026-08-07T16:00:00+00:00", "v": 10185.89}, {"t": "2026-08-07T20:00:00+00:00", "v": 10180.56}, {"t": "2026-08-08T00:00:00+00:00", "v": 10198.29}, {"t": "2026-08-08T04:00:00+00:00", "v": 10186.99}, {"t": "2026-08-08T08:00:00+00:00", "v": 10189.03}, {"t": "2026-08-08T12:00:00+00:00", "v": 10206.6}, {"t": "2026-08-08T16:00:00+00:00", "v": 10205.51}, {"t": "2026-08-08T20:00:00+00:00", "v": 10188.09}, {"t": "2026-08-09T00:00:00+00:00", "v": 10160.79}, {"t": "2026-08-09T04:00:00+00:00", "v": 10154.75}, {"t": "2026-08-09T08:00:00+00:00", "v": 10143.08}, {"t": "2026-08-09T12:00:00+00:00", "v": 10187.77}, {"t": "2026-08-09T16:00:00+00:00", "v": 10179.8}, {"t": "2026-08-09T20:00:00+00:00", "v": 10135.89}, {"t": "2026-08-10T00:00:00+00:00", "v": 10149.02}, {"t": "2026-08-10T04:00:00+00:00", "v": 10184.96}, {"t": "2026-08-10T08:00:00+00:00", "v": 10199.49}], "total_trades": 2, "generated_at": "2026-08-10T08:30:34.346421+00:00", "asset": "BTC"}
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{"strategy": "grid_mm", "interval": "4h", "n_bars": 8, "start_equity": 10000.0, "end_equity": 10043.91, "total_return_pct": 0.44, "pnl": 43.91, "sharpe": 9.31, "sortino": 16.098, "max_drawdown_pct": 0.51, "win_rate": NaN, "profit_factor": NaN, "expectancy": NaN, "trades": [{"time": "2026-08-10 08:00:00", "side": "BUY", "asset": "BTC", "size": 0.153873, "entry_px": 64978.91, "exit_px": 65274.0, "pnl_gross": 43.906, "pnl_net": 40.8996, "fee": 3.006361, "fee_rate_pct": 0.015, "return_pct": 0.439, "duration": ""}], "params": {"grid_levels": 10, "grid_spacing_pct": 0.1, "rebalance_every": 20, "type": "Market Making"}, "fee_info": {"entry_fee": 0.015, "exit_fee": 0.015, "total_fee": 0.03, "taker_rate_pct": 0.045, "maker_rate_pct": 0.015, "effective_rate_pct": 0.015, "vip_tier": 0, "staking_tier": "none", "fee_model": "maker", "tier_name": "Tier 0 (<$5M)", "staking_name": "No Stake"}, "equity_curve": [{"t": "2026-08-09T04:00:00+00:00", "v": 10000.0}, {"t": "2026-08-09T08:00:00+00:00", "v": 9988.51}, {"t": "2026-08-09T12:00:00+00:00", "v": 10032.52}, {"t": "2026-08-09T16:00:00+00:00", "v": 10024.67}, {"t": "2026-08-09T20:00:00+00:00", "v": 9981.43}, {"t": "2026-08-10T00:00:00+00:00", "v": 9994.36}, {"t": "2026-08-10T04:00:00+00:00", "v": 10029.75}, {"t": "2026-08-10T08:00:00+00:00", "v": 10043.91}], "total_trades": 1, "generated_at": "2026-08-10T08:30:37.651418+00:00", "asset": "BTC"}
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