Below a short list of the most important algo-trading parameters, that most traders use to develop their strategies.

## DrawDown -> Minimize!

In the context of algo trading, drawdown refers to the decline in the value of a portfolio or trading account. It is typically measured as the percentage decline from a recent peak in the value of the portfolio or account. For example, if the value of a portfolio increases from \$100,000 to \$120,000 and then decreases to \$110,000, the drawdown would be calculated as (120,000 – 110,000) / 120,000 = 8.3%.

Drawdown is an important metric to consider when evaluating the performance of a trading strategy or portfolio, as it gives an indication of the risk that has been taken on. A high drawdown indicates that the portfolio or account has experienced significant losses, which can be stressful for the trader and potentially jeopardize the long-term viability of the strategy. On the other hand, a low drawdown indicates that the portfolio or account has been relatively stable and less risky.

It is important to note that drawdown is not the same as volatility, which measures the amount of price movement in a security or portfolio. Volatility is typically measured using standard deviation, while drawdown is a measure of the actual decline in value.

## NetProfit-drawdown ratio: Ret/DD aka NP/DD -> Minimize!

The Net Profit-Drawdown (NP/DD) ratio, also known as the Return-Drawdown (Ret/DD) ratio, is a measure of the risk-adjusted performance of a trading strategy or portfolio. It is calculated by dividing the net profit of the strategy or portfolio by the maximum drawdown experienced over a given period of time.

For example, if a trading strategy has generated a net profit of \$100,000 and has experienced a maximum drawdown of \$20,000, the NP/DD ratio would be calculated as 100,000 / 20,000 = 5.0. A higher NP/DD ratio indicates that the strategy or portfolio has generated a higher return for a given level of risk, while a lower NP/DD ratio indicates the opposite.

The NP/DD ratio is often used by traders and investors to compare the performance of different strategies or portfolios and to assess the risk-reward tradeoff of a particular strategy. It is important to note that the NP/DD ratio is sensitive to the length of the analysis period and may not be a reliable indicator of future performance.

In the context of futures trading, the spread refers to the difference between the bid price and the ask price of a futures contract. The bid price is the highest price that a buyer is willing to pay for a contract, while the ask price is the lowest price that a seller is willing to accept for a contract. The difference between the bid and ask prices is known as the spread.

The spread in futures trading represents the cost of entering into a futures contract, as a trader must pay the ask price to buy the contract and will receive the bid price when selling the contract. The size of the spread can vary depending on a number of factors, including the liquidity of the market, the supply and demand for the contract, and the risk profile of the underlying asset.

Traders can profit from the spread by buying a contract at the bid price and then selling it at the ask price, or by selling a contract at the ask price and then buying it back at the bid price. This is known as scalping, and it can be a useful strategy for traders looking to generate small profits from short-term price movements.

## Slippage (Cost of Trading COT) -> Minimize!

In the context of futures trading, slippage refers to the difference between the expected price of a trade and the actual price at which the trade is executed. Slippage can occur when there is a discrepancy between the bid and ask prices of a contract, or when the market moves unexpectedly while a trade is being executed.

For example, if a trader places a market order to buy a futures contract at the current bid price, but the price of the contract increases before the trade is executed, the trader will end up paying a higher price than expected. This difference between the expected price and the actual price is known as slippage.

Slippage can be positive or negative, depending on whether the actual price of the trade is higher or lower than the expected price. It is an important consideration for traders, as it can have a significant impact on the profitability of a trade. Traders can minimize the impact of slippage by using limit orders instead of market orders and by carefully monitoring market conditions.

## Commissions (Cost of Trading COT) -> Minimize!

Commissions refer to the fees that a trader or investor pays to a broker or financial institution for executing trades on their behalf. Commissions are typically charged as a percentage of the trade value or as a fixed fee per trade.

Commissions are a common way for brokers and financial institutions to generate revenue, and they can vary significantly depending on the type of asset being traded and the brokerage firm or institution involved. Some brokers and financial institutions offer reduced commissions or other incentives to attract and retain customers, while others may charge higher commissions for certain types of trades or services.

It is important for traders and investors to carefully consider the impact of commissions on their trading strategy or portfolio, as they can have a significant impact on the profitability of trades. Many traders and investors try to minimize their commission costs by using discount brokers or by trading assets with lower commission rates. Some brokers and financial institutions also offer volume discounts or other incentives for traders and investors who execute a large number of trades.

## Walk-Forward Efficiency -> Maximize!

In the context of system trading, walk forward efficiency is a measure of the performance of a trading strategy over time. It is typically calculated by comparing the performance of a strategy during a “walk forward” period, which is a series of overlapping time periods, to the performance of the strategy during a “hold out” period, which is a non-overlapping time period following the end of the walk forward period.

The walk forward efficiency of a strategy is typically expressed as a percentage, with a higher percentage indicating a more efficient strategy. For example, if a strategy has a walk forward efficiency of 80%, this means that the strategy has performed 80% as well during the hold out period as it did during the walk forward period.

The walk forward efficiency of a trading strategy is an important consideration for traders, as it gives an indication of how well the strategy is likely to perform in the future. A strategy with a high walk forward efficiency is more likely to continue to perform well in the future, while a strategy with a low walk forward efficiency may be less reliable.

In the context of trading, the average trade refers to the average profit or loss of a series of trades over a specific period of time. It is calculated by taking the total profit or loss of all the trades in the series and dividing it by the number of trades.

The average trade is a useful metric for traders and investors to track the performance of their trading strategy or portfolio. It can help to identify trends and patterns in the performance of the strategy or portfolio and can be used to assess the risk-reward tradeoff of the strategy.

For example, if a trader has made 10 trades over the past month and has generated a total profit of \$1,000, the average trade would be calculated as 1,000 / 10 = \$100. This would indicate that the trader has been generating an average profit of \$100 per trade over the past month.

It is important to note that the average trade is a statistical measure and may not accurately reflect the performance of individual trades. It is also important to consider other factors such as the risk taken on and the consistency of the performance when evaluating the performance of a trading strategy or portfolio.

## Curve-fitting -> Minimize!

Curve fitting refers to the process of designing a model or trading strategy that is specifically tailored to fit the characteristics of a particular data set. It involves adjusting the model or strategy to achieve the best possible fit to the data, often by adding more variables or parameters.

While a model or strategy that has been curve fitted may perform well on the data set it was designed for, it may not generalize well to other data sets or market conditions and may not be a reliable indicator of future performance. This is because the model or strategy has been specifically designed to fit the characteristics of the data set, rather than being derived from more general principles that are applicable across a range of data sets and market conditions.

Curve fitting is a common problem in trading, as it is often tempting to try to achieve the best possible performance on a particular data set. However, it is important to be mindful of the risks of overfitting and to test a model or strategy on out-of-sample data to ensure that it is robust and reliable. There are several techniques that can be used to minimize the risk of curve fitting, such as using cross-validation, backtesting, and walk forward analysis.

## Strategy-complexity -> Minimize!

In the context of trading, complexity refers to the number of variables or factors that a trading strategy or model takes into account when making decisions. A strategy or model with a high level of complexity may be more difficult to understand and implement, but it may also be more effective at capturing the nuances of the market.

On the other hand, curve fitting refers to the process of designing a model or strategy that is specifically tailored to fit the characteristics of a particular data set. While a model or strategy that has been curve fitted may perform well on the data set it was designed for, it may not generalize well to other data sets or market conditions and may not be a reliable indicator of future performance.

In general, it is important to strike a balance between complexity and curve fitting when designing a trading strategy or model. A strategy or model that is too simple may not capture the nuances of the market and may not perform well, while a strategy or model that is overly complex or has been heavily curve fitted may be prone to overfitting and may not generalize well to other market conditions.

## Strategy correlation -> Minimize!

In the context of a strategy portfolio, strategy correlation refers to the degree to which the performance of different trading strategies is related to one another. A high degree of correlation between two strategies means that the performance of one strategy is closely tied to the performance of the other, while a low degree of correlation means that the performance of the two strategies is relatively independent of one another.

Strategy correlation can be calculated using statistical techniques such as Pearson’s correlation coefficient. A correlation coefficient of +1 indicates a perfect positive correlation, meaning that the two strategies move in the same direction at the same time. A correlation coefficient of -1 indicates a perfect negative correlation, meaning that the two strategies move in opposite directions at the same time. A correlation coefficient of 0 indicates no correlation, meaning that the performance of the two strategies is independent of one another.

Understanding the correlation between different strategies in a portfolio can be useful for traders and investors, as it can help to identify strategies that may be redundant or that may not add value to the portfolio. For example, if two strategies in a portfolio have a high degree of positive correlation, it may be more efficient to use only one of the strategies rather than both. On the other hand, if the strategies have a low degree of correlation, they may be complementary and can add diversification to the portfolio.

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