To Trade Or Not To Trade: Cascading Waterfall Round Robin Rebalancing Mechanism for Cryptocurrencies

ArXiv ID: 2407.12150 “View on arXiv”

Authors: Unknown

Abstract

We have designed an innovative portfolio rebalancing mechanism termed the Cascading Waterfall Round Robin Mechanism. This algorithmic approach recommends an ideal size and number of trades for each asset during the periodic rebalancing process, factoring in the gas fee and slippage. The essence of the model we have created gives indications regarding whether trades should be made on individual assets depending on the uncertainty in the micro - asset level characteristics - and macro - aggregate market factors - environments. In the hyper-volatile crypto market, our approach to daily rebalancing will benefit from volatility. Price movements will cause our algorithm to buy assets that drop in prices and sell as they soar. In fact, the buying and selling happen only when certain boundaries are crossed in order to weed out any market noise and ensure sound trade execution. We have provided several numerical examples to illustrate the steps - including the calculation of several intermediate variables - of our rebalancing mechanism. The Algorithm we have developed can be easily applied outside blockchain to investment funds across all asset classes at any trading frequency and rebalancing duration. Shakespeare As A Crypto Trader: To Trade Or Not To Trade, that is the Question, Whether an Optimizer can Yield the Answer, Against the Spikes and Crashes of Markets Gone Wild, To Quench One’s Thirst before Liquidity Runs Dry, Or Wait till the Tide of Momentum turns Mild.

Keywords: portfolio rebalancing, trading algorithm, gas fee, slippage, crypto market, Cryptocurrency

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats
  • Why: The paper employs standard financial mathematics (volatility calculations, weight optimization) with some advanced concepts like risk parity and time-series models, but relies primarily on numerical examples rather than backtested data, lacking empirical performance metrics or real-world implementation details.
  flowchart TD
    A["Research Goal<br>To Trade Or Not To Trade?"] --> B["Key Methodology<br>Cascading Waterfall Round Robin<br>(Gas & Slippage Aware)"]
    
    B --> C["Data Inputs<br>Micro-Asset &<br>Macro-Market Factors"]
    
    C --> D["Computational Process<br>Optimization Logic<br>Trade Boundaries &<br>Volume Calculation"]
    
    D --> E{"Outcome Decision<br>Trade or Wait?"}
    
    E -- "Execute" --> F["Key Findings<br>Optimal Rebalancing<br>Hyper-Volatile Benefit"]
    E -- "Hold" --> F