adba9f3fe8f9be958cc99fbdfb329277aa2c0ecd
E2E exercise results:
30 episodes × 30 steps = 900 actions
CWM: HftBacktestCWM (PowerProbQueueModel)
Swarm: 11 diverse opponents (2x ToxicTaker, 2x PassiveMaker, LatencyArb,
Noise, Momentum, MeanReversion, InventoryMM, LiquidationFlow,
StaleQuoteAttacker)
Performance:
Avg PnL: +7217.9 bps, Win rate: 66.7% (20/30)
Best: +26171.0 bps (chop scenario)
Worst: -5.0 bps (thin/arb scenarios — flat, not loss)
Order types exercised:
LIMIT 16.9%, MARKET 12.9%, STOP_MARKET 0.2%
IOC 8%, GTC 92%
Post-only: path exercised, Reduce-only: 41.5%
Aggression/Passive ratio: 0.76
Speed: 457 actions/second (2.0s total)
Description
Sentiment analysis engine with ONNX FinBERT + LoRA adapters
Languages
Python
99.9%
Dockerfile
0.1%