Swing Trader: Consumer, Energy and Financial Sectors (Diversified)

This AI robot is created for swing traders focused on trading a diversified set of stocks from the consumer, energy, and financial sectors. This approach makes it possible to ensure resistance to a sharp change in market conditions for a particular industry and to have a balanced set of open positions.

The algorithm of robot consists of two parts:

1. Analysis of the correlation between the direction of movement of main stocks and other stocks included in this sector. This method (the analysis of correlated (similar) stocks) is one of the most popular approaches for creating trading strategies used by hedge funds. In the process of creating this robot, our team of quants conducted multi-level backtests on a larger amount of historical data. This allowed us to identify correlations relationships between the leaders of the sector and the rest of the stocks included in it.
2. Creation of an optimal diversification model based on a quantitative analysis of the efficiency of various combinations of sectors. The main goal was to allow our users not to depend on market cycles and external events that can negatively affect the dynamics in a particular industry.

This robot trades stocks of companies from the consumer, energy, and financial sectors of the economy. This combination allows our users to have a balanced set of positions in terms of volatility and be resistant to changes in market and industry trends.

The average duration of a trade is only 3 days, which allows our users not to stuck in one position for a long time and effectively use capital. The maximum number of open trades is 36, which ensures good diversification to reduce the impact of one trade on overall profitability.

After entering the trade, the AI Robot places a fixed order "Take profit" at the level of 4% of the position opening price. To exit the trade, the robot uses a fixed stop loss of 4% of the position opening price, which allows our users to avoid large drawdowns.

The robot's trading results are shown without using a margin. For a full trading statistics and equity chart, click on the "show more" button on the robot page. In the tab “Open Trades”, a user can see live how the AI Robot selects equities, enters, and exits in paper trades. In the tab “Closed trades”, a user can review all previous trades made by the AI Robot.

About sectors:

The consumer sector includes companies involved in the production of food, clothing, beverages, automobiles, electronics, and many other goods used in everyday life. Companies such as Coca-Cola, Procter & Gamble, Walmart are known all over the world, and their stocks are included in the portfolios of most investment funds. At the same time, consumer stocks have large differences in volatility and price dynamics, which requires the use of reliable trading algorithms.

The main business of companies from the energy sector is related to the exploration, development and processing of oil, gas, and coal reserves. This sector also includes companies engaged in renewable energy and power generation. Stocks of such companies as Chevron, Exxon Mobile are popular among traders due to high volatility and good trendiness.

The financial sector consists of a wide range of industries, including banks (major and regional), investment companies, different types of insurance companies (Life, Property, Specialty Insurance) etc. Among the well-known companies in this sector, there are both companies with a long history that provide traditional types of financial services, such as Bank of America, Goldman Sachs, and new fintech companies that create financial services of the future - SoFi, Robinhood. Accordingly, financial stocks also have large differences in volatility, price dynamics, which requires the use of reliable algorithms for trading them

Disclaimer. The presented paper traded results (annualized returns, % wins/loss, and other statistics) are achieved by the application of the backtested and forward tested models. The past backtested and forward tested performance should not be taken as an indication or guarantee of future performance, and no representation or warranty, expressed or implied is made regarding future performance. Forward testing started on 01.25.2023.