Reinforcement learning (RL) for foreign exchange (Forex) trading requires realistic execution modeling, interpretable reward design, and action spaces that respect market constraints. Existing approaches often simplify these aspects, limiting policy transparency and practical relevance. This paper presents a modular RL framework integrating three components: (1) a friction-aware execution engine enforcing anti-lookahead semantics---observations at close_t, execution at open_t+1, and mark-to-market at close_t+1---while modeling spread, commission, slippage, rollover financing, and margin-triggered liquidation; (2) a decomposable 11-component reward architecture with fixed, pre-specified weights and per-step logging, enabling attribution, ablation, and auditability; and (3) a 10-action discrete interface supporting scaling, reduction, closure, and reversal, with legal-action masking enforcing margin-aware feasibility during training and evaluation. Empirical evaluation on EURUSD training-split data reveals non-monotonic interactions among reward components, demonstrating that additional penalties do not consistently improve performance. The full reward configuration achieves a terminal training Sharpe ratio of 0.765 and cumulative return of 57.09%. Comparing action spaces, the extended 10-action interface increases cumulative return but reduces Sharpe relative to a conservative 3-action variant, revealing a return-activity trade-off under a fixed training budget. Scaling-enabled strategies consistently reduce drawdowns, with the combined configuration delivering the strongest endpoint performance. Supplementary diagnostics on GBPUSD, USDJPY, and AUDUSD exhibit qualitatively similar trends. These findings demonstrate the framework's diagnostic value and underscore the importance of controlled, interpretable design in RL-based financial systems.
Classical theory predicts that irrational traders lose money to rational traders and are eventually driven out of the market. Yet noise trading, herding, and heuristic trading persist even in highly developed markets. We model a financial market as an ecology of two trading-behavior types, relatively rational (R) and boundedly rational (N), whose composition evolves under replicator dynamics according to relative performance. Each type's fitness depends on the share of the other type and on frictions. When the interaction is complementary, that is, rational traders profit from the mispricing created by boundedly rational traders while the latter benefit from the liquidity and price correction supplied by the former, and when the difference in frictions is moderate, a unique interior composition exists and is globally asymptotically stable: bounded rationality persists endogenously. When the interaction is antagonistic, the same point becomes a tipping threshold between two one-type regimes. Comparative statics show that lowering the costs of disciplined trading, as algorithmic and AI-based trading does, shifts the composition toward rational trading continuously but does not eliminate bounded rationality unless the cost advantage exceeds an explicit threshold.
Financial assistants answer questions that sound factual but depend on what the user leaves unsaid: what a data figure measures, which product variant is meant, and the investor's situation and the rules that apply. An assistant that ignores these can still answer with a real number from its data, one that answers a different question. We call this frame collapse. We study it in Indian mutual funds, where product variants and tax rules are formally defined, so the correct answer can be computed for every reading. One study tests each challenge. Given an ambiguously labelled figure, models used it, often under the user's label. Given every alternative labelled, they listed them all, but the layout decided which came first. Missing a fact about the investor, weaker agents assumed a default or an outdated tax rule; requiring the fact did not guarantee its use. Because such answers match their data, checks against the data pass them. An AI judge caught mislabelled figures only when told what each field means, and still passed answers that silently chose another reading. We show how these failures arise, not how often, and propose controls for financial assistants.