Introducing AlgoReturns

AlgoReturns (https://www.algoreturns.com/trident-signal-menu/) is a futures trading research firm founded by quant trading professionals with decades of experience. In their own words:

”AlgoReturns (Algo) offers a unique value proposition for the futures trading community.  The Company has developed a turnkey solution with a family of algorithmic trading products that covers the entire spectrum of the needs of the players involved in futures trading including FCM’s, CTA’s, brokers and traders, both retail and institutional.”

AlgoReturns is partnering up with Paloma to leverage the Pyth-Paloma Limit Order Bot (LOB) and related infrastructure services. AlgoReturns develops trading strategies in both the TradFi and DeFi space, and pursues a pragmatic approach to the development of a trading strategy instead of a strict theoretical framework.

In this blog post, we walk you through AlgoReturns’ core philosophies of risk management. A foundation to AlgoReturns’ pragmatic approach is a “risk first” paradigm – capital preservation is more important than capital growth. More specifically, trading risk is set immediately after the position is activated and the level of risk is determined by proprietary algorithms and are optimized for modest risk tolerance. In addition, we demonstrate how AlgoReturns’ strategies can be extended to the DeFi space when partnering with Paloma’s scheduling and execution infrastructure. We present a sample strategy as a model for combining AlgoReturns’ strategy and the Pyth-Paloma Limit Order Bot (LOB).

Systematic Risk Management

Everyone likes a winning trade, but smart players carefully analyze losing trades. Drawdowns (losses) are an inevitable fact of trading but managing drawdowns can be a source of differentiation among traders. There is an implicit tradeoff between frequency of drawdowns vs. severity of drawdowns. Setting tight risk tolerances will lead to frequent premature exits denying trades their much-needed breathing room. Conversely, wide risk limits can generate a singular large drawdown that wipes out several months of accumulated winnings.

Setting an appropriate profit target can entail similar tradeoff considerations too. Trivial winnings do not add significantly to the capital cushion while the pursuit of large winnings increases the chance of market reversals. Traders’ behavior as it relates to weathering favorable or adverse trade positions is idiosyncratic over a wide spectrum of possibilities. Finally markets and financial instruments can vary greatly, and their nuances are not always well understood by investors. Leveraged products such as derivatives, carry a significant downside risk that is exacerbated in uncertain market conditions. Participants in emerging DeFi markets face similar challenges and can benefit from well-reasoned trading strategies.

The core of AlgoReturns’ strategy is to systematically manage both downside and upside risks. Every recommended trade always sets the trade risk immediately after the position is activated. The level of risk is determined by proprietary algorithms and is optimized for modest risk tolerance. With the downside risk under pragmatic control (e.g., stop-loss, as mentioned above), the strategy can shift focus to the realization of upside potential. The upside potential comprises 2 parts – a fixed target and a variable target. A fixed target is a reasonable expectation of modest favorable market movement, and a variable target is deliberately open-ended to facilitate the pursuit of an opportunistic movement in favor of the trade. The pursuit of a variable target is activated when the initial capital outlay for the trade has already been earned back and realized, thereby adding no incremental risk to the position.

Leveraging the Pyth-Paloma LOB

AlgoReturns’ pursuit of risk control by setting profit-taking and stop-loss limits fits perfectly with the Pyth-Paloma LOB. As introduced in our earlier blog posts, the Pyth-Paloma LOB is an on-chain, scheduler-based application. It provides an end-to-end synthetic limit order service in the DeFi space. Here we describe a sample AlgoReturns strategy trading ETH denominated in USDC, and how it may be implemented with the Pyth-Paloma LOB.

A Sample Strategy

Alice is a crypto trader using one of AlgoReturns’ trading strategies that seeks exposure to the propensity of prices to be mean reverting. The signals are created by analysis on price distribution of ETH, which give trade entry price points. The strategy is set based on that her risk appetite is conservative with no more than 1% of her capital exposed on a single trade.

The strategy is to go long when ETH market price reaches 10 day rolling 12.5 percentile, as the expectation is that prices will display a propensity to revert to the mean. The strategy expects a reasonably upward movement in price while controlling for downside risk. Simulating this event as of Aug 11 2022, 10 day rolling 12.5 percentile of ETH is 1616.66 and ETH price drops to that level triggering and entrance. And Alice’s risk tolerance is at 1% which translates to a downside risk limit of 1600.50 (1616.660.99).*

The strategy also aims to maximize her profit potential. She is willing to pursue a modest target to fully mitigate the risk in the trade and only after which she is willing to opportunistically pursue a higher target. To mitigate the risk in the trade, her target must be slightly higher than the risk, say 1.1% which translates to a price target of 1634.44 (1616.66*1.011). Once this price is achieved, the risk on the trade can be effectively set to 0.

The following are implemented on Pyth-Paloma:

• ETH market data feed from Pyth

• A set of strategy parameters including entrance price [1616.66], downside limit [1600.50] and upside limit [1634.44] configured on Paloma

• A Paloma scheduler for monitoring entrance price, once its hit:

  • switches the scheduler to a Pyth-Paloma LOB with two exits: profit taking and stop-loss

  • once either of the limits is hit, update the strategy parameters and switches the scheduler to monitor the next entrance

Summary

In this post, we showcased a systematic trading team, AlgoReturn’s approach to build strategies and risk management. Their signal generation process could benefit from Paloma’s scheduling and monitoring infrastructure and data feed handily. Moreover, the Pyth-Paloma LOB’s synthetic limit order feature is tailor made for their disciplinary risk management system. We hope this post provides a good use case for any up-and-coming DeFi trading team to collaborate with Paloma and help us build more interesting applications.

We’re extremely excited to continue building our platform and to have you all along for the journey. Come build on Paloma!

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