In Step 6, a risk analysis will be performed. This type of analysis exposes uncertainties and risks we face in trading and investing. Your trading robot is trained to mitigate or hedge risks associated with investing. Risk is also reduced during the symbol selection process in Step 7.

Traders and investors are in the business of taking risks every day. Risk analysis and risk management are crucial. First, risk management helps prevent the loss of investment capital. A rigorous risk analysis reveals areas for improvement to increase performance and profitability. It reduces the fluctuation of the value of your investment portfolio. That way, your account will have a smoother movement towards profit. Our risk analyst and data analyst take note of how other financial professionals analyze risk incorrectly and avoid making the same mistakes. Completing Step 6 also helps validate that Step 2, Step 3, and Step 5 were completed correctly.

**Quantitative Risk Analysis**

Quantitative risk analysis is the practice of creating a mathematical model of a project or process that explicitly includes uncertain parameters that we cannot control, and also decision variables or parameters that we can control. A quantitative risk model calculates the impact of the uncertain parameters and the decisions the trading robot makes on the outcomes that we care about – such as profit and loss and the volatility of the portfolio.

**Monte Carlo Simulation and Quantitative Risk Analysis**

Named after the city in Monaco famed for its casinos and games of chance — is a powerful mathematical method for conducting quantitative risk analysis. Monte Carlo methods rely on random sampling — the computer-based equivalent of a coin toss, dice roll, or roulette wheel. The numbers from random sampling are “plugged into” a mathematical model and used to calculate outcomes. This process is repeated thousands of times. With the aid of software, we obtain statistics and view charts and graphs of the results. Monte Carlo simulations can quickly analyze thousands of 'what-if' scenarios, often yielding surprising insights into what can go right, what can go wrong, and what we can do about it.

**Risk Budget**

Our risk analyst quantifies the financial risks involved in each investment and trading activity and allocates a risk budget. Risk budgeting is a mathematical approach that brings logic and science to the portfolio management process. Risk budgeting is the process of identifying, quantifying, and “spending risk” in the most efficient manner possible. The process helps smooth the equity curve.

**Risk Management and Simulation Optimization**

Simulation Optimization goes one step further than just helping us understand risk to allow us to make better decisions taking into account that risk. We do this by building a model where for each decision choice we run a Monte Carlo simulation, record the results and then continue to test additional decisions until we reach an optimal solution.

**Drawdown Tolerance Bias**

This particular phenomena is not often discussed in the context of quantitative trading. However, it is discussed extensively in regard to more discretionary trading methods. When creating backtests over a period of 5 years or more, it is easy to look at an upwardly trending equity curve, calculate the compounded annual return, Sharpe ratio and even drawdown characteristics and be satisfied with the results. As an example, the strategy might possess a maximum relative drawdown of 25% and a maximum drawdown duration of 4 months. This would not be atypical for a momentum strategy. It is easy to think that an investor will wait out the drawdown knowing that the trading robot will recover. However, in practice, it is much harder!

If historical drawdowns of 25% or more occur in the backtests, then in all likelihood you will see periods of similar drawdown in live trading. These periods of drawdown are psychologically difficult to endure. Often a strategy which would otherwise be successful is stopped from trading during times of extended drawdown and thus will lead to significant underperformance compared to a backtest. Thus, even though the strategy is algorithmic in nature, psychological factors can still have a heavy influence on profitability. The takeaway is to ensure that if you see drawdowns of a certain percentage and duration in the backtests, then you should expect them to occur in live trading environments, and will need to persevere in order to reach profitability once more. Our risk analyst will also optimize the parameters to limit drawdown.

**Avoid the Gaussian copula function**

The Gaussian copula family of models are used in finance to estimate the probability distribution of losses on a portfolio. They were centrally involved in the credit crisis and is commonly referred to as 'The Formula That Killed Wall Street'.

**Kelly Criterion**

The Kelly Criterion helps determine the optimal size of trades (or positions), which may help maximize long-term returns while minimizing the risk of ruin.

**New Sharpe Ratio**

The "new Sharpe Ratio" highlights the impact of asymmetrical returns and provides a more accurate measure of risk-adjusted returns than the traditional Sharpe Ratio.

At the completion of this step, you will receive a risk analysis that will be included in the due diligence documents. It will include a description of the testing methodology. It will include the optimal parameters values to use to get the best risk adjusted returns.