Where does DL beat the classic Quant?
DL impresses with fast pricing/risk through neural surrogates, short-term forecasts from order book data (LSTM/GRU) and cost-conscious hedging with reinforcement learning.
How much data is required – and might synthetic data help?
Use as much clean, labeled gradient as possible. Fill gaps with VAEs/GANs for scenario expansion and data protection, then validate against withheld real-world data.
Can you trust the Greeks and the chance of neural prices?
Yes, in case you use differential training (prices and sensitivities), don’t force arbitrage/monotonicity, and monitor Greek drift in production.
How can we meet latency constraints in production?
Train offline; Deploying compact models on GPUs/CPUs (or FPGAs for ultra-low latency); cache results; and be used as drop-in surrogates alongside current pricing providers.
What satisfies model risks and regulators?
Model risk teams and regulators are satisfied once you deliver models with built-in explainability (feature mappings, sensitivity testing), documented data lineage, lively champion challenger setups (challenger models), proven stability across market regimes, and explicit, enforced usage restrictions.
Does RL work live?
It can, if trained with realistic costs/liquidity and operated with guardrails (position limits, kill switches, stress triggers) in addition to continuous post-trade monitoring.
