Exchange Rate Fixing System Development

Development of Exchange Rate Fixing System

Rate fixing (rate lock / rate fixing) is a mechanism to guarantee a specific exchange rate for a certain time. User sees rate 1 BTC = 50,000 USDT and has 10 minutes to execute the deal at exactly that rate, regardless of market movement. For the exchanger it's a risk—the market can move against you. For the user—it's predictability.

Why Rate Fixing is Needed

Without fixing: user starts exchange, sends BTC, during this time rate drops 2%, gets fewer USDT than expected. Negative UX, complaints, churn.

With fixing: user sees guaranteed amount, knowingly makes decision, gets exactly what was promised. Trust and retention are higher.

Rate Fixing System Architecture

Rate Lock Structure

from dataclasses import dataclass
from decimal import Decimal
from datetime import datetime, timedelta
import uuid

@dataclass
class LockedRate:
    lock_id: str
    from_currency: str
    to_currency: str
    from_amount: Decimal
    to_amount: Decimal          # guaranteed amount
    rate: Decimal               # locked rate
    market_rate_at_lock: Decimal  # market rate at lock time
    our_margin: Decimal         # our margin in to_currency
    locked_at: datetime
    expires_at: datetime
    status: str = 'active'      # active / used / expired / cancelled

class RateLockService:
    def __init__(self, price_feed, margin_calculator, risk_manager):
        self.price_feed = price_feed
        self.margin_calc = margin_calculator
        self.risk = risk_manager

    async def create_rate_lock(
        self,
        from_currency: str,
        to_currency: str,
        from_amount: Decimal,
        lock_duration_seconds: int = 600
    ) -> LockedRate:
        # Get current market rate
        market_rate = await self.price_feed.get_rate(from_currency, to_currency)

        # Calculate our margin with volatility buffer
        margin = self.margin_calc.calculate(
            from_currency=from_currency,
            to_currency=to_currency,
            from_amount=from_amount,
            lock_duration=lock_duration_seconds
        )

        # Guaranteed rate = market rate minus our margin
        locked_rate = market_rate * (1 - margin)
        to_amount = from_amount * locked_rate

        lock = LockedRate(
            lock_id=str(uuid.uuid4()),
            from_currency=from_currency,
            to_currency=to_currency,
            from_amount=from_amount,
            to_amount=to_amount.quantize(Decimal('0.000001')),
            rate=locked_rate,
            market_rate_at_lock=market_rate,
            our_margin=from_amount * market_rate - to_amount,
            locked_at=datetime.utcnow(),
            expires_at=datetime.utcnow() + timedelta(seconds=lock_duration_seconds)
        )

        # Check risk: don't take too many locks in one direction
        if not await self.risk.can_accept_lock(lock):
            raise RiskLimitExceeded("Rate lock rejected by risk manager")

        await self.db.save_lock(lock)
        return lock

Margin Calculation with Volatility Adjustment

Fixed margin is inefficient: in calm market we take too much, in volatile—too little.

class DynamicMarginCalculator:
    def calculate(
        self,
        from_currency: str,
        to_currency: str,
        from_amount: Decimal,
        lock_duration: int  # seconds
    ) -> Decimal:
        # Historical volatility (last 24h)
        vol_24h = self.get_volatility(from_currency, to_currency)

        # Expected move during lock period
        # For normal distribution: sigma_t = sigma_daily * sqrt(t/86400)
        expected_move = vol_24h * (lock_duration / 86400) ** 0.5

        # Take 2-sigma (95% coverage) + base margin
        safety_margin = expected_move * 2
        base_margin = Decimal('0.003')  # 0.3% minimum

        # Volume discount for large orders
        volume_discount = Decimal('0.001') if from_amount * self.get_price(from_currency) > 10000 else Decimal('0')

        return max(base_margin, Decimal(str(safety_margin))) - volume_discount

Managing Risk for Locked Rates

If many users simultaneously lock rates in one direction (all want to sell BTC), the exchanger accumulates directional risk:

class RateLockRiskManager:
    def __init__(self, max_net_exposure_usd: float = 100_000):
        self.max_net_exposure = max_net_exposure_usd

    async def can_accept_lock(self, lock: LockedRate) -> bool:
        # Calculate current total exposure
        active_locks = await self.db.get_active_locks()

        net_exposure = sum(
            float(l.from_amount) * float(l.rate)
            if l.from_currency == lock.from_currency
            else -float(l.from_amount) * float(l.rate)
            for l in active_locks
        )

        new_exposure = float(lock.from_amount) * float(lock.rate)
        total_exposure = abs(net_exposure + new_exposure)

        return total_exposure < self.max_net_exposure

    async def hedge_if_needed(self, lock: LockedRate):
        """Hedge large locks on external exchange"""
        threshold_usd = 5000

        if float(lock.from_amount) * float(lock.rate) > threshold_usd:
            await self.exchange.hedge_position(
                currency=lock.from_currency,
                amount=lock.from_amount,
                direction='buy' if lock.from_currency == 'USDT' else 'sell'
            )

Expiration and Invalidation

async def cleanup_expired_locks(self):
    """Periodically clean up expired locks"""
    expired = await self.db.get_expired_active_locks()

    for lock in expired:
        await self.db.update_lock_status(lock.lock_id, 'expired')

        # Release hedged position
        if lock.was_hedged:
            await self.exchange.close_hedge(lock.lock_id)

    logger.info(f"Expired {len(expired)} rate locks")

async def use_rate_lock(self, lock_id: str, actual_from_amount: Decimal) -> ExchangeResult:
    lock = await self.db.get_lock(lock_id)

    if lock.status != 'active':
        raise LockNotActive(f"Lock {lock_id} is {lock.status}")

    if datetime.utcnow() > lock.expires_at:
        await self.db.update_lock_status(lock_id, 'expired')
        raise LockExpired("Rate lock has expired")

    # Allow small deviation in amount (±1% of locked)
    amount_deviation = abs(actual_from_amount - lock.from_amount) / lock.from_amount
    if amount_deviation > Decimal('0.01'):
        raise AmountMismatch("Amount differs by more than 1% from locked amount")

    # Recalculate proportionally if amount differs slightly
    actual_to_amount = actual_from_amount * lock.rate

    await self.db.update_lock_status(lock_id, 'used')
    return ExchangeResult(
        from_amount=actual_from_amount,
        to_amount=actual_to_amount,
        rate=lock.rate,
        lock_id=lock_id
    )

Frontend Timer Display

const RateLockTimer: React.FC<{expiresAt: Date; onExpired: () => void}> = ({
  expiresAt, onExpired
}) => {
  const [secondsLeft, setSecondsLeft] = useState(0);

  useEffect(() => {
    const update = () => {
      const left = Math.max(0, Math.floor((expiresAt.getTime() - Date.now()) / 1000));
      setSecondsLeft(left);
      if (left === 0) onExpired();
    };
    update();
    const timer = setInterval(update, 1000);
    return () => clearInterval(timer);
  }, [expiresAt]);

  const isUrgent = secondsLeft < 60;

  return (
    <div className={`flex items-center gap-2 ${isUrgent ? 'text-red-500 animate-pulse' : 'text-gray-600'}`}>
      <ClockIcon />
      <span>Rate locked for {Math.floor(secondsLeft/60)}:{String(secondsLeft%60).padStart(2,'0')}</span>
    </div>
  );
};

A rate fixing system is a balance between user experience and financial risk. Too short period (2-3 minutes)—poor UX, users don't have time. Too long (30+ minutes)—high exchanger risk in volatile market. Optimal for crypto: 10-15 minutes with dynamic margin.