Strategy Backtesting And Optimization 

At Marsbridge Capital, we consider strategy backtesting and optimization as essential components of algorithmic trading development, aimed at evaluating and refining trading strategies to enhance performance and profitability. Here's an overview of what our services entail:

 

Backtesting

 

We evaluate trading strategies for our clients using historical market data to assess its performance and effectiveness in simulated or past market conditions.

Process:

Data Collection: Historical market data relevant to the trading strategy, including price data, volume, and other relevant indicators, is collected.

Strategy Implementation: The trading strategy is coded into an algorithm or trading system that can execute trades based on predefined rules and criteria.

Simulation: The strategy is applied to historical market data to simulate trading activity over a specified period.

Performance Evaluation: The performance of the strategy is evaluated based on key metrics such as profitability, risk-adjusted returns, maximum drawdown, win rate, and others.

 

Optimization

 

We assist our clients in fine-tuning the parameters of a trading strategy to improve its performance based on historical data.

Process:

Parameter Identification: The parameters of the trading strategy that can be adjusted or optimized are identified. These may include indicators, thresholds, timeframes, and other variables.

Optimization Algorithm: Optimization algorithms, such as grid search, genetic algorithms, or simulated annealing, are used to systematically explore different combinations of parameters.

Performance Evaluation: Each set of parameters is backtested using historical data, and the performance metrics are evaluated to determine the optimal combination.

Sensitivity Analysis: Sensitivity analysis is performed to assess the impact of parameter changes on strategy performance and identify robust parameter ranges.

Validation: The optimized strategy is validated using out-of-sample data or walk-forward analysis to ensure that the improvements observed are not the result of overfitting.