Learning from Peso Stability
And the Stablecoins trend
Last week I talked about how I tried to short a position USD/MXN assuming that the change of administration was going to increase the spread in this pair of currencies. The fun thing is I haven’t even sold pesos has gained 30 cents over USD dollars after the change of administration and it’s been stable since then. I think this is great don’t get me wrong, but to make out of my short-term losses I decided to dig more into Forex. Because this substack is about Blockchain and AI, I want to relate this conversation to the success of stablecoins in mainstream during the last few weeks, I think the adoption of key players like Visa will increase the supply of stablecoins in the market bringing more liquidity into the blockchain ecosystem.
So how do we being blockchain folks need to understand what is behind forex, and how we can leverage algorithmic stablecoins to set up a trading strategy?
What are Algorithmic Stablecoins
Cryptocurrencies are designed to maintain a stable value, typically pegged to a fiat currency like the US dollar, without being backed by actual reserves. Instead of holding collateral, these stablecoins rely on algorithmic mechanisms to maintain price stability through supply and demand adjustments. Here's how it works in trading:
Supply and Demand Mechanisms: Algorithmic stablecoins use smart contracts to automatically increase or decrease the supply of coins based on market demand, keeping their prices stable. When the stablecoin price exceeds the peg (e.g., $1), the algorithm issues more coins to reduce the price. Conversely, when the price drops below the peg, it reduces the supply by incentivizing users to burn coins in exchange for rewards or other tokens.
Rebalancing Mechanisms: Many algorithmic stablecoins use a two-token or even three-token model—one for the stablecoin and another governance token (e.g., Terra's LUNA before its collapse). The governance token helps absorb the volatility of the stablecoin by adjusting its supply to re-establish the peg.
How does Trading Algorithmic Stablecoins Work?
Arbitrage Trading: Traders profit by taking advantage of price discrepancies between the stablecoin and its peg. For example, if an algorithmic stablecoin trades above $1, traders can sell it for a profit. If it drops below $1, they can buy the stablecoin at a discount and wait for the algorithm to restore the peg, realizing gains when the price returns to $1.
Bond or Coupon Mechanism: Some algorithmic stablecoins (e.g., Basis Cash) issue bonds or coupons that traders can buy when the stablecoin’s price falls below the peg. These bonds are bought at a discount and are redeemable once the price stabilizes, allowing traders to profit when the peg is restored.
What are the Risks in Algorithmic Stablecoin Trading?
Depegging Risk: Algorithmic stablecoins can fail to maintain their peg if market conditions create too much selling pressure. For instance, a sudden drop in demand may make it hard for the algorithm to reduce supply fast enough, leading to a loss of confidence and further price drops.
Death Spiral Risk: If traders lose confidence in the algorithm, a feedback loop can occur, where they begin dumping both the stablecoin and the governance token, leading to a “death spiral” and collapse of the system. This happened with TerraUSD (UST) in 2022.
4. Trading Strategies
Here is a template for implementing a trading strategy using pathfinding algorithms to trade stablecoins and Forex.
Data Collection:
Gather real-time data on exchange rates for USDT, USDC, DAI, USD, MXN, EURO, BTC, and ETH. You can build a dashboard in Dune to monitor economic indicators and news that may affect currency volatility.
Graph Construction:
Construct a graph where each node represents a currency pair. Define edges based on transaction costs (spreads), liquidity (depth of market), and risk factors (volatility).
Pathfinding Execution:
Use Dijkstra's Algorithm to find initial optimal paths for basic trades. Implement A* Search Algorithm to refine these paths dynamically based on real-time market conditions.
Trade Execution:
Execute trades along identified paths while continuously monitoring market conditions. Adjust strategies based on feedback from executed trades and ongoing analysis.
Risk Management:
Incorporate stop-loss orders and take-profit levels based on volatility assessments derived from algorithm outputs. Regularly review and adjust trading parameters to reflect changing market dynamics.
So far we’ve learned that to build an effective stack for forex trading we need:
A Visualisation dashboard
A visual graph
Two or three search and optimization algorithms
Two automation and execution algorithms.
Remember to understand this as a technical nerdy exercise rather than financial advice.
Appreciate that "L" here is a Learning 😤