Add comprehensive inline documentation to RFQ examples and channel models

[Copilot]

Added explanatory comments throughout the RFQ codebase to clarify implementation intent, trading strategies, and risk management concepts.

Changes

RFQ Examples

• 00_create_rfq.py: Documented taker workflow (RFQ creation → quote subscription → execution), callback mechanisms, and direction reversal logic
• 01_simple_rfq_quoter.py: Explained market maker role, mark-price-based pricing strategy, quote lifecycle tracking, and concurrent RFQ processing
• 02_delta_hedged_quoter.py: Documented delta-neutral strategy, portfolio exposure calculation across asset types (options/perps/spot), automated hedging thresholds, and event-driven rebalancing

Channel Models

• Added docstrings to RFQ/quote-related schemas (QuoteResultPublicSchema, WalletRfqsChannelSchema, SubaccountIdBestQuotesChannelSchema) explaining WebSocket subscription patterns and data flow

Example

Before:

async def price_rfq(self, rfq):
    # Price legs using current market prices
    priced_legs = []
    for unpriced_leg in rfq.legs:
        ticker = await self.client.markets.get_ticker(...)
        base_price = ticker.mark_price
        price = base_price * D("0.999") if unpriced_leg.direction == Direction.buy else base_price * D("1.001")

After:

async def price_rfq(self, rfq):
    """
    Price the legs of an RFQ based on current market prices.
    
    This simple implementation uses mark price with a small spread:
    - For buy legs (we're selling to the taker): mark_price * 1.001 (0.1% markup)
    - For sell legs (we're buying from the taker): mark_price * 0.999 (0.1% discount)
    
    A real market maker would consider order book depth, volatility Greeks,
    inventory positions, and competition.
    """

Comments focus on why decisions were made (strategy rationale, risk considerations) rather than restating code mechanics.

---

💬 We'd love your input! Share your thoughts on Copilot coding agent in our 2 minute survey.