FAQ

Algohubb Rating is a measure created to evaluate individual strategies as objectively as possible. For each strategy, we assess the following criteria: Average monthly profit (MM$) Return/Drawdown ratio (MM%) Maximum percentage Open Drawdown (MM%) Winrate Exposure Robustness - evaluation based on the number of transactions appropriate for the strategy type and the strategy's resilience to changing conditions. Each criterion is assigned a weight in our overall evaluation. The sum of weighted results contributes to the Algohubb Rating. Please note that as backtests are updated based on the latest data, the rating of a particular strategy may change over time. To maintain objectivity in the evaluation, we also reserve the right to develop the definition of the rating by changing the weights of individual criteria and adding or replacing current criteria.

This difference occurs when testing the same strategy with different test parameters. For example, in one test, the result for the same strategy's Ret/DD ratio is 8, while in another test, it is 12. The result generally depends on: Instrument: It is natural to obtain different results when testing different instruments. Date range included in the study: If you want to obtain an analogous backtest for the same instrument, you need to select the same date range. Money Management type: We apply both MM$ and MM% in backtests, and these measures will yield different results in both types of tests. Some measures are only meaningful for a specific type of test. For example, the Ret/DD % ratio is meaningful only if we choose MM%. Costs: Costs can have a fundamental impact on the profitability of a strategy. Therefore, take the time to choose an appropriate broker and analyze their costs. You can refer to our research in this area. Testing engine: Differences in versions of the Algocloud or SQX engine are usually marginal but can occur depending on the version. All our tests are conducted on the SQX platform (at the time of writing, v138.1827). Historical data and their corrections: In the historical data of individual stocks, errors are sometimes detected and corrected, such as incorrect adjustments for dividends or splits. Rarely occurring errors like these should be reported to the data provider. If you have checked the above and still have doubts about the test results in our strategies, don't hesitate to get in touch with us, and we will try to help!

To start your adventure with algorithmic trading, you will need the following tools: Below we present the tools that we use and that we can recommend as comprehensive and very well covering the topic. 1. Strategy A strategy is like an employee, you hire them, but you should thoroughly check them before trusting and assigning responsible tasks. You should also know, how to combine their work with other strategies/employees so that they form an effective team (strategy portfolio). You should also know when to let them go/stop or replace them with a more efficient one. Unlike real employees, you don't have to pay algorithms a salary or insurance, you don't have to have strong emotions associated with them, they can work for you 24/7, they don't have bad days (in general), and they perform exactly as well as they were created and tested. We make every effort to ensure that you find in Algohubb the best strategies and knowledge on how to use them effectively. Strategy is code or a ready-to-use file. Downloading a file of a chosen strategy from Algohubb is simple and free for most of the strategies published by us. 2. Testing Platform To use a strategy, you need to trust it. This can be achieved by conducting your own backtest of the strategy to understand its workings, strengths, and weaknesses. Popular testing tools that also serve as live trading platforms include Meta Trader, Tradestation, Multicharts, and StrategyQuant/Algocloud. We thoroughly used each of these platforms. Each of them has its advantages and weaknesses. To get into the details, check out our platform comparison. In many aspects, we chose the Algocloud platform, which is an extension of StrategyQuant - the best backtesting platform in our experience. Algocloud has several fundamental advantages over the others. One of them is the Stockpicker mechanism, which can scan even 2000+ stocks simultaneously and place orders on the most promising setups. Algocloud has a very good (identical in basic functions to StrategyQuant) backtesting engine. For basic testing and starting your journey, an Algocloud account is sufficient and free. For professional usage, we can recommend adding StrategyQuant to it. 3. Demo and Live Trading Platform Choose your platform. As mentioned above, Algocloud is a platform we highly recommend for demo and live trading. Opening a demo account in Algocloud is also simple and free. 4. Brokerage Firm To trade on the stock exchange, you need to open an account with a brokerage firm connected to the exchange. I have tested dozens of brokers. When it comes to stocks and algo trading, I have never come across a better broker than Alpaca (I am only sharing my personal opinion and I am not involved in any Alpaca affiliate program). They offer true $0 commission, tight market spreads, no fixed account fees, and access to all important instruments like US Equity and ETFs. There are also excellent connections with platforms like Algocloud and TradingView. This is everything an algorithmic trader trading stocks needs from a broker. Opening a demo and real account in Alpaca is also simple and free. 5. Monitoring and Charting Platform. Algocloud and Alpaca provide convenient panels for monitoring algorithms, but for detailed chart analysis, TradingView is recommended as the best charting platform, which seamlessly integrates with Alpaca, and its free in the Essential plan. In summary, these are the proven tools you can try and use in the process:

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