Strategy Design and Baktesting environment

Rumi Finance places a strong emphasis on developing and refining robust yield strategies using advanced techniques, such as artificial intelligence (AI), to ensure their effectiveness. To achieve this, Rumi employs a comprehensive strategy design and back testing environment, which includes an on-chain simulation environment. This allows the Rumi Finance team to test, analyze, and optimize various strategies before deploying them in the live DeFi ecosystem.

The back testing environment simulates market conditions and applies historical data to validate the performance of proposed strategies. It also offers a modular approach, enabling the team to analyze specific aspects of liquidity provisioning, delta hedging, liquidity range optimization, and portfolio creation, among others.

Key components of the strategy design and back testing environment include:

• Data Collection: Rumi Finance gathers historical market data, including price movements, liquidity pool data, and protocol-specific metrics, which are essential for back testing and strategy design.

• AI Integration: Artificial intelligence is used to analyze large datasets, identify patterns, and optimize strategies based on complex algorithms and machine learning models, ensuring the strategies adapt to changing market conditions.

• On-chain Simulation Environment: Rumi Finance utilizes an on-chain simulation environment to replicate real-world market conditions and test strategies in a more accurate and secure manner.

• Strategy Simulation: The Rumi Finance team develops algorithms and strategies, simulating their performance using historical data to evaluate their effectiveness under various market conditions.

• Modular Back Testing: The back testing process is designed to be modular, allowing the team to analyze specific components of strategies, such as liquidity provisioning, delta hedging, and liquidity range optimization, as well as create and evaluate portfolios.

• Performance Metrics: Key performance indicators, such as yield, Sharpe ratio, and maximum drawdown, are analyzed to assess the risk-adjusted returns of each strategy.

• Optimization: Rumi Finance continually refines and optimizes the strategies based on the back testing results and performance metrics. This iterative process helps ensure that the strategies deployed in the live environment are robust and adaptive to changing market conditions.

• Risk Framework: The back testing environment enables Rumi Finance to assess and manage risks associated with the strategies. By understanding the potential risks, the team can make informed decisions and adjust the strategies to minimize potential losses and optimize returns.

Back testing Framework

Rumi Finance's backtesting framework is a comprehensive toolset that allows for the evaluation and optimization of various yield strategies. The framework integrates a wide range of mathematical models, machine learning algorithms, and visualization tools to test and refine strategies before deploying them in live DeFi environments. The backtesting framework consists of the following components:

Mathematical Models: Rumi Finance has developed an extensive inventory of mathematical models for executing and analyzing strategies, including liquidity pools (UniV2 and UniV3), impermanent loss modeling, hedging models, UniV3 price prediction for range optimization, liquidity pool slippage, rewards modeling, liquidation thresholds, and lending & leveraged borrowing models.

Rebalancer: A generic rebalancing function that receives instructions on how to rebalance strategies based on various parameters and signals.

PnL Construction: The framework processes historical data to generate historical PnL and a rich dataset of parameters.

Data Visualization: The backtesting framework enables the visualization of output data, allowing for the charting of any data point, such as PnL data generated from a Delta Neutral Simulation.

ARIMA Modeling: Rumi Finance uses a fine-tuned ARIMA model and AI to generate future historical data for scenario analysis, creating thousands of historical paths to better simulate models and estimate potential future returns and behavior.

Quantitative Optimization: The framework processes the generated paths using a quantitative optimization technique to determine the best rebalancing parameters and evaluate if the strategy has the desired risk-reward profile. Metrics such as CVaR, Sharpe ratio, and drawdown values are used to quantify the profiles.

Portfolio Optimization: If the strategy involves a set of aggregated strategies, the backtesting framework consolidates the results from step 6 and runs them through a portfolio optimization process using Risk Parity optimization. This process determines the correct portfolio composition and provides risk metrics such as CVaR, Sharpe ratio, and drawdown values to quantify the portfolio's profile.

Rumi Finance's backtesting framework allows the team to evaluate and refine yield strategies comprehensively, ensuring that they are optimized and well-suited for deployment in live DeFi environments. By utilizing AI, advanced mathematical models, and optimization techniques, Rumi Finance can provide users with high-performing, risk-adjusted yield strategies that meet their investment goals.