Desirable Performance Behaviour - The BOTS app
September 1, 2022

Desirable Performance Behaviour - The BOTS app

The objective of this document is to share with you what kinds of behaviors can differentiate desirable from undesirable performance.

Desirable Performance Behaviour

Different measures are employed depending on the bot's strategy/approach.


On the platform, a substantial number of bots follow a "Portfolio rebalancing strategy." These bots redistribute the weighting of funds over a portfolio of assets for investors that want to invest in a particular group of coins. App users are more likely to subscribe to them due to fundamental reasons instead of performance-based reasoning.


For users who want to hold a particular asset, our platform offers "holders" as an option. As a result, there are no significant performance limitations. If there are consistent and large drawdowns, beyond the 60% mark, or discrepancies between traded assets and descriptions, BOTS would take action to intervene.

Trading Bots

The best part begins with trading bots on our platform. At BOTS, we believe that every strategy should offer an edge over a basic holding strategy. We also want to make sure that strategies can appropriately manage risk and shield our users from major dips occurring in the crypto market.

To satisfy our goals, we employ two different methods:

  • The Benchmark Analysis - A performance comparison of a bot against a defined benchmark.
  • The Stand-Alone Analysis - An evaluation of a bot's activity and capacity to manage risk.

Benchmark Analysis

In the first stage, all assets traded during the sandbox need to be identified. A portfolio that consists of all these assets will be created, and position percentages will be taken into account. If at any point the percentage changes for different trades in a single asset, then BOTS will use the last one as input.

Comparing behavior with the benchmark:

  • Jensen’s Alpha -  Alpha is a parameter of the Capital Asset Pricing Model (CAPM). It's one of the most important measures for determining how well a fund will do compared to a benchmark. Alpha quantifies the outperformance of a trading strategy after taking into account risk and market factors.
  • RAROC - Risk Adjusted Average Return On Capital - This is a risk-based profitability measurement framework for evaluating risk-adjusted financial performance. Here we compare the trading strategies’ RAROC with the benchmark RAROC. If the trading strategies' ratio is better than the benchmarks' one, it means that the bot is outperforming the benchmark.

Overall, a bot is outperforming the benchmark whenever the two ratios produce positive results. Even if there are times when a bot does not meet these standards, BOTS will not automatically remove it from the platform. Of course, market circumstances may change and there could be periods in which lower performance is seen; however, this does not mean intervention will take place. We become worried when a bot falls short of the performance benchmark for multiple months in a row, especially if market conditions look stable. If this happens, we investigate further by studying the strategy's behaviors and its capacity to manage risk.

Stand-Alone Analysis

The standalone risk and performance evaluation put a lot of pressure on the bot's ability to manage and control risk. Too much volatility in returns significantly raises the chance of failure.

BOTS constantly monitor the following parameters:

  • ROI - This is the most basic and frequent performance indicator. It's a calculation of net income against invested funds.
  • A bot's goal is to create long-term returns. A bot that has been unable to produce any profit in the previous 4-5 months might be an indication of inconsistency.
  • Max drawdown - The drawdown is the biggest loss between a high and a low on an equity curve. A low number is ideal.
  • Maximum drawdowns should not exceed 40%. BOTS feels that significant drawdowns are a potential for bankruptcy.
  • Total return/max drawdown - This tells us how much money a bot has made relative to how much it has lost. It's intended to show the potential profit-to-loss connection. The greater the value, the better.
  • The ideal total return over maximum drawdown would be greater than 1, meaning there is a greater potential for gain than loss.
  • Realized Volatility - Realized volatility shows how much an investment's returns fluctuate by analyzing its past performance over a specific period.
  • Realized Volatility aims to be below a certain threshold to confine strategies with volatile return variations. This value helps provide context, especially when worry builds about the bot's ability to manage risk.

All in all

BOTS is aware that there are other ways to measure and monitor performance beyond the models we currently use in our decision making. Other methods and ratios are being studied for implementation, especially for those Bots that have been in the platform for over a year, from which we have sufficient data to use other metrics.

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