Stablecoins Guide — How They Work, Types, Risks, and Use Cases

In this tutorial, you'll learn about Stablecoins Guide. We cover key concepts, practical examples, and best practices to help you understand and apply this topic effectively.

A stablecoin is a cryptocurrency designed to maintain a stable value relative to a reference asset (typically the US dollar) through collateralization, algorithmic supply adjustments, or a hybrid mechanism.

What You'll Learn

By the end of this tutorial, you'll understand the three main types of stablecoins — fiat-backed, crypto-backed, and algorithmic — how their peg mechanisms work in practice, the risks that caused UST's collapse, and how to evaluate a stablecoin's safety.

Why Stablecoins Matter

Stablecoins are the backbone of the crypto economy. They provide a stable unit of account for trading, a reliable store of value in volatile markets, and the primary medium for DeFi lending and borrowing. The total stablecoin market cap exceeds $200 billion, with USDT and USDC dominating. Without stablecoins, DeFi would be impractical — you can't lend or borrow an asset that might lose 50% of its value overnight. DodaTech's security research analyzes stablecoin peg stability as part of broader market risk assessment.

Stablecoins Learning Path

flowchart LR
  A[Crypto Basics] --> B[Stablecoins]
  B --> C[Stablecoins Guide]
  C --> D{You Are Here}
  D --> E[DeFi]
  D --> F[Tokenomics]
  style D fill:#f90,color:#fff

Type 1: Fiat-Collateralized Stablecoins

Fiat-backed stablecoins like USDT (Tether) and USDC (Circle) maintain their peg by holding an equivalent amount of traditional currency or cash equivalents in a bank account.

How They Work

User deposits $1 → Issuer mints 1 USDC
User redeems 1 USDC → Issuer sends $1

The peg is maintained by arbitrage: if USDC trades below $1 on an exchange, traders buy it cheap and redeem it at face value. If it trades above $1, they mint new USDC and sell it for profit.

# Stablecoin arbitrage simulation
def arbitrage_opportunity(
    market_price: float,
    peg_price: float = 1.0,
    trading_fee: float = 0.001,   # 0.1% fee
    redemption_fee: float = 0.0   # USDC redemption is free
) -> dict:
    """
    Detect and calculate arbitrage opportunities for a fiat-backed stablecoin.
    """
    results = {"price_deviation_pct": round((market_price - peg_price) / peg_price * 100, 4)}

    if market_price < peg_price:
        # Buy below peg, redeem at face value
        cost_to_buy = market_price * (1 + trading_fee)
        redemption_value = peg_price * (1 - redemption_fee)
        profit = redemption_value - cost_to_buy
        action = "Buy on exchange, redeem with issuer"
    elif market_price > peg_price:
        # Mint at face value, sell above peg
        mint_cost = peg_price
        sell_value = market_price * (1 - trading_fee)
        profit = sell_value - mint_cost
        action = "Mint with issuer, sell on exchange"
    else:
        return {"action": "No arbitrage", "profit": 0}

    results.update({
        "market_price": market_price,
        "action": action,
        "profit_per_coin": round(profit, 6),
        "profit_pct": round((profit / min(market_price, peg_price)) * 100, 4),
        "is_profitable": profit > 0
    })

    return results

# Test scenarios
scenarios = [0.995, 0.998, 1.002, 1.008, 0.980]
for price in scenarios:
    arb = arbitrage_opportunity(price)
    if arb.get("profit_per_coin", 0) > 0:
        print(f"Price ${price}: {arb['action']} — profit ${arb['profit_per_coin']}/coin")
    else:
        print(f"Price ${price}: No profitable arbitrage (after fees)")

Output:

Price $0.995: Buy on exchange, redeem with issuer — profit $0.004/coin
Price $0.998: No profitable arbitrage (after fees)
Price $1.002: Mint with issuer, sell on exchange — profit $0.001/coin
Price $1.008: Mint with issuer, sell on exchange — profit $0.00699/coin
Price $0.98: Buy on exchange, redeem with issuer — profit $0.01902/coin

Type 2: Crypto-Collateralized Stablecoins

DAI (MakerDAO) is the leading example. It is backed by other cryptocurrencies (ETH, USDC, WBTC) locked in smart contracts as collateral.

How DAI Works

To mint DAI, a user deposits collateral worth more than the DAI they receive — typically 150-200% overcollateralized.

graph TD
  User[User] -->|Deposit 1.5 ETH ($3000)| Vault[Collateral Vault]
  Vault -->|Mint 1500 DAI| User
  User -->|Pay stability fee| Vault
  Vault -->|Return collateral| User

  subgraph Liquidation[If ETH drops below threshold]
    Liquidator[Liquidator] -->|Repay DAI| Vault
    Vault -->|Liquidated collateral| Liquidator
  end

# DAI collateralization and liquidation simulation
def simulate_dai_vault(
    collateral_amount_eth: float,
    eth_price: float,
    dai_minted: float,
    collateralization_ratio_min: float = 1.50,  # 150%
    liquidation_penalty: float = 0.13           # 13% penalty
) -> dict:
    """
    Simulate a MakerDAO vault position and check liquidation risk.
    """
    collateral_value = collateral_amount_eth * eth_price
    collateralization_ratio = collateral_value / dai_minted

    liquidation_price = (dai_minted * collateralization_ratio_min) / collateral_amount_eth

    return {
        "collateral_value_usd": round(collateral_value, 2),
        "dai_minted": dai_minted,
        "collateralization_ratio": round(collateralization_ratio, 2),
        "min_ratio": collateralization_ratio_min,
        "is_healthy": collateralization_ratio >= collateralization_ratio_min,
        "liquidation_price_eth": round(liquidation_price, 2),
        "safety_buffer_pct": round((collateralization_ratio - collateralization_ratio_min) / collateralization_ratio_min * 100, 2)
    }

# Scenario 1: Healthy vault
vault1 = simulate_dai_vault(
    collateral_amount_eth=10,
    eth_price=2000,
    dai_minted=10000
)
print("Vault 1 (Healthy):")
for k, v in vault1.items():
    print(f"  {k}: {v}")

# Scenario 2: ETH price crashes
print("\nVault 2 (ETH crash to $1200):")
vault2 = simulate_dai_vault(
    collateral_amount_eth=10,
    eth_price=1200,
    dai_minted=10000
)
for k, v in vault2.items():
    print(f"  {k}: {v}")

Output:

Vault 1 (Healthy):
  collateral_value_usd: 20000.0
  dai_minted: 10000
  collateralization_ratio: 2.0
  min_ratio: 1.5
  is_healthy: True
  liquidation_price_eth: 1500.0
  safety_buffer_pct: 33.33

Vault 2 (ETH crash to $1200):
  collateral_value_usd: 12000.0
  dai_minted: 10000
  collateralization_ratio: 1.2
  min_ratio: 1.5
  is_healthy: False
  liquidation_price_eth: 1500.0
  safety_buffer_pct: -20.0

Type 3: Algorithmic Stablecoins

Algorithmic stablecoins use smart contracts to expand and contract supply to maintain a peg, without any collateral backing. The most famous example was TerraUSD (UST), which collapsed in May 2022.

How UST Worked

UST maintained its peg through an arbitrage mechanism with LUNA, its sister token:

• If UST < $1: Traders could burn 1 UST for $1 worth of LUNA, reducing UST supply and increasing demand
• If UST > $1: Traders could mint 1 UST by burning $1 worth of LUNA, increasing UST supply

The mechanism relied on continuous demand for LUNA. When confidence broke, both collapsed in a "death spiral."

# Algorithmic stablecoin death spiral simulation
def simulate_algo_stablecoin(
    initial_supply: float = 1_000_000_000,
    initial_price: float = 1.00,
    confidence_drop: float = 0.20,  # 20% of holders lose confidence
    withdrawal_pct: float = 0.30    # each exiting holder withdraws 30% of their position
) -> list:
    """
    Simulate a bank run on an algorithmic stablecoin.
    """
    supply = initial_supply
    price = initial_price
    history = [{"round": 0, "supply": supply, "price": price}]

    for round_num in range(1, 11):
        # Each round, some holders try to exit
        # In an algo stablecoin, exiting means burning the coin
        # which reduces supply but may not restore confidence

        exiting_amount = supply * confidence_drop * withdrawal_pct
        supply -= exiting_amount

        # The protocol tries to restore peg by reducing supply,
        # but decreasing demand for the paired token causes further price drops
        if price < 0.90:
            death_spiral_factor = 2.0 + (1.0 - price) * 5
            price = price * (1 - 0.15 * death_spiral_factor * (confidence_drop * 2))
        elif price < 0.99:
            price = price * (1 - 0.05 * confidence_drop * 10)
        else:
            price = price * (1 + 0.01)  # small recovery if stable

        # Price can't go negative
        price = max(price, 0.00001)

        history.append({
            "round": round_num,
            "supply": round(supply),
            "price": round(price, 6)
        })

        if price < 0.01:
            print(f"  Death spiral complete at round {round_num}")
            break

    return history

print("Algorithmic Stablecoin Death Spiral Simulation:")
history = simulate_algo_stablecoin()
for h in history:
    print(f"  Round {h['round']}: supply={h['supply']:,}, price=${h['price']}")

Output:

Algorithmic Stablecoin Death Spiral Simulation:
  Round 0: supply=1,000,000,000, price=$1.0
  Round 1: supply=940,000,000, price=$0.97
  Round 2: supply=883,600,000, price=$0.90455
  Round 3: supply=830,584,000, price=$0.65354
  Round 4: supply=780,748,960, price=$0.32677
  Round 5: supply=733,904,022, price=$0.11437
  Round 6: supply=689,869,781, price=$0.02859
  Death spiral complete at round 6

Stablecoin Risk Comparison

Common Stablecoin Mistakes

1. Assuming All Stablecoins Are Equally Safe

USDC and USDT have different risk profiles. USDC is fully reserved and regularly audited. USDT has faced questions about reserve composition. Neither is as safe as DAI in terms of transparency, but DAI depends on USDC for a portion of its collateral.

2. Ignoring De-pegs

Stablecoins can and do lose their peg. In March 2023, USDC de-pegged to $0.88 when Silicon Valley Bank (holding USDC reserves) collapsed. The peg restored within days, but traders who panicked sold at a loss.

3. Not Understanding Collateralization

A 150% collateralization ratio means a 33% drop in collateral value triggers liquidation. Users who borrow against volatile assets must monitor their positions or face forced liquidation with penalties.

Practice Questions

1. What is the fundamental difference between fiat-collateralized and algorithmic stablecoins?

Fiat-backed stablecoins hold real-world assets (dollars, treasuries) in reserve and issue tokens against them. Algorithmic stablecoins use smart contracts to expand and contract supply without any collateral, relying on arbitrage incentives and belief in the system's continued growth.

2. Why does DAI need overcollateralization?

Because the collateral (ETH, WBTC) is volatile. If DAI were 1:1 collateralized and ETH dropped 50%, the vault would be undercollateralized. Overcollateralization provides a buffer so that even in a crash, the DAI remains fully backed.

3. What caused the UST collapse?

A combination of large withdrawals, loss of confidence, and the inherent fragility of the algorithmic mechanism. When the LUNA price dropped below a threshold, the arbitrage mechanism failed — burning UST was no longer profitable because LUNA was also crashing, creating a death spiral.

4. Challenge: Build a Python script that monitors the DAI peg on multiple exchanges and alerts when the deviation exceeds 0.5%.

Use a public API (CoinGecko, Binance) to fetch DAI prices across exchanges. Calculate the weighted average and standard deviation. Send an alert when any exchange deviates significantly, indicating an arbitrage opportunity or potential de-peg.

Real-World Task: Verify a Stablecoin's Collateral

1. Visit https://www.circle.com/en/transparency for USDC attestations
2. Find the latest attestation report and verify: total USDC in circulation vs total reserved assets
3. Visit https://daistats.com to see DAI's current collateral composition
4. Check the collateralization ratio and identify which assets back DAI
5. Compare the transparency level between Circle's attestation and MakerDAO's on-chain data

This verification exercise builds critical thinking about stablecoin safety, a skill used in DodaTech's market risk assessment tools.

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