Backtesting strategy is a statistical simulation technique used by traders to determine a trading strategy's output. The simulation makes use of historical market evidence to determine the performance of a trading policy in the past.
Best Cryptocurrency Backtesting Platforms
Backtesting strategy is a statistical simulation technique used by traders to determine a trading strategy's output. The simulation makes use of historical market evidence to determine the performance of a trading policy in the past. Without further ado, here is a list of the best cryptocurrency backtesting platforms:
6 Besat Cryptocurrency Backtesting Softwares
- Best all-in-one cryptocurrency trading software: Altrady
- Best for Python Developers: Binance
- Best open-source resource: Gekko
- Best for developers using Node.js and MongoDB: Zenbot
- Best overall alternative: Holderlab
- Most Readily Available Software: Microsoft Excel
Backtesting is a technique used by traders to determine whether a policy can be profitable when applied with actual money. Backtesting is utilized by traders to exclude strategies that have not been profitable previously.
Though past success is not a promise of potential outcomes, backtesting remains the most accurate method for identifying robust strategies. It is important to review these models to exclude underperforming techniques. This way, we maximize our odds of profit and avoid the need to validate tactics with real capital.
Backtesting has grown in popularity as cryptocurrency trading tools have grown in popularity. Today, traders are advised to carefully backtest all strategies prior to launching them into the wild crypto market. This way, we can have faith in the strategy's prospects for optimum performance.
Why Backtesting Strategy?
Backtesting is a critical component of developing an efficient trading strategy. It is done by reconstructing trades that may have happened in the past using the rules established by a given strategy using historical evidence. The outcome provides statistics on the strategy's success.
The fundamental theory is that any strategy that has performed well in the past is likely to do well in the future, and vice versa with any strategy that has performed badly in the past.
Backtesting is a critical component of designing a trading scheme. If designed and interpreted correctly, it will assist traders in optimizing and improving their tactics, identifying technological or theoretical shortcomings, and gaining trust in their approach before implementing it in real-world markets.
1. Altrady - backtesting to be introduced in Q1 of 2022
"Boost your trading with the all-in-one cryptocurrency trading software."
The cryptocurrency sector operates twenty-four hours a day, seven days a week. As a result, keeping abreast of the market conditions becomes tiresome. Rather than fighting the bots, you should collaborate with them by using Altrady's professional portfolio automation options.
The strength of Altrady is how it simplifies and streamlines trading. After subscribing to the website and successfully integrating your trading accounts, you can immediately begin taking advantage of its many features. Take advantage of their smart trading, real-time market data and alerts, connectivity to multiple exchanges, and scaled ladders to optimize your cryptocurrency trading experience. This makes the platform the best overall backtesting platform on the list.
In comparison to other trading sites, Altrady focuses only on the most common and well-known techniques. They will assist you with automating portfolio rebalancing, dollar-cost averaging, and stop-loss management. Any one of these approaches has proven effective over time.
Altrady makes it easier than ever to trade as an institution or skilled investor. Based on historical data, you can analyze your trading efficiency. You can get an analysis of your trading results with Altrady's trading analytics, which can help you determine the efficacy of your trading strategy.
One of the most appealing features of Altrady's bitcoin portfolio manager is its well-organized data presentation. You can display details through a line chart for the USD/BTC portfolio valuation, a pie chart for currency and exchange spread, or a stacked chart for comparing the amount of a single coin to the overall asset.
You should also backtest the plans in the backtesting suite before allocating a portfolio using real-time market data. It's the ideal method for determining the long-term effect of various automation environments.
Tip for developers: The most popular method for developers to introduce a backtesting tool is to use OHLCV candlestick results. The majority of developers make use of this data because it is easily accessible. Unfortunately, while this is the simplest data to access for the purposes of developing these methods, it is still the most unreliable data. Indeed, backtesting with OHLCV candlestick data can mean the difference between developing a profitable plan and losing money.
2. Python Backtesting on Binance
"Use past market data to see how a strategy would have performed."
The following is a trading environment in which all potential trading techniques can be checked in a highly competitive manner, allowing even the most inexperienced Python programmer to build and backtest their trading concepts, eventually providing them with an answer to their questions.
Backtesting on Binance follows the conventional logic. So, we are only going to follow through with a backtesting illustration that can be replicated on the platform.
To begin, we purchase one Bitcoin on the first regular close following a golden cross. If you're wondering what a golden cross is, fear not. A golden cross (or golden crossover) is a chart trend characterized by the passing of a short-term moving average over a long-term moving average. The 50-day moving average is often used as the short-term average, while the 200-day moving average is used as the long-term average.
Then, at the first regular close after a death cross, we sell one Bitcoin. Once more, a death cross is the polar opposite of a golden cross. It is a chart trend in which a short-term moving average crosses below a long-term moving average. For instance, the 50-day moving average crosses below the 200-day moving average. As a result, a death cross is sometimes regarded as a bearish signal.
The golden and death crosses denote the time period during which the technique is accurate.
The illustration considers only the time frame from the beginning of 2019 to the present. However, if you want more precise and consistent results, you can go even farther back in time in Bitcoin's price history.
See also: Best Crypto Games
"A free and open-source Bitcoin TA trading and backtesting platform."
Gekko is an open-source trading project that allows for the collection of real-time market data, the estimation of metrics, and the simulation of order execution.
Because of the operation's nature, it is almost necessary to have a deep technical understanding of trading, programming, and exchanges in order to use this trading resource.
Gekko was designed to allow programmers to develop their own trading strategies using custom technical analysis indicators.
Regrettably, since Gekko is not a hosted service, you will have to keep your machine running 24 hours a day to fully utilize this trading platform.
After a strategy has been implemented in Gekko, it can be backtested using the same repository's paper trader.
"A command-line cryptocurrency trading bot using Node.js and MongoDB."
Zenbot is comparable to Gekko in several respects. It is mainly an open-source trading bot that developers can use to create their own trading strategies.
You can use historical data to backtest the trading strategies you create. Although this is beneficial, Zenbot, like Gekko, will rely heavily on OHLCV data, which is arguably unreliable.
Apart from the backtesting simulator, Zenbot has a paper trading feature that enables developers to simulate trading using real-time market data. This is perfect for evaluating emerging ideas against historical consumer data.
"Automated cryptocurrency portfolio management platform."
Backtesting cryptocurrencies against historical evidence enables one to evaluate the cryptocurrency portfolio's dynamics. Holderlab.io's backtest module enables you to evaluate the selected technique.
This section will walk you through the process of backtesting your techniques on this platform.
Step 1: Navigate to the backtest section; here, you can either upload your portfolio through the "load portfolio" button or build/pick a portfolio by selecting and adding cryptocurrencies.
Step 2: Once you've chosen the cryptocurrency portfolio to test, you'll need to enter the required weight (distribution in percent) for each cryptocurrency; the total weight of all cryptocurrencies should be 100% if a lower or higher percentage does not function. Additionally, you can add and delete cryptocurrencies by clicking the "X" and "+" keys.
Step 3: Then, press the "start" button to specify a testing date, a rebalancing plan, and initial money.
Step 4: After seeing the test results, you should examine the graph, which depicts the entire portfolio's testing – capitalization and each of the portfolio's cryptocurrencies.
Step 5: Using the slider, you can toggle between $ USD and BTC, as well as turn on and off individual coins on the portfolio map.
Step 6: Finally, you'll receive a summary of the crypto portfolio's performance and a detailed analysis of each cryptocurrency by parameter.
6. Microsoft Excel
"Excel learns your patterns, organizing your data to save you time."
The majority of people are familiar with and have access to Microsoft Excel. Its user-friendly features, as well as a wealth of online resources on how to use it, make the entire backtesting process easier. So, where and how does one begin? The first step is to collect a testable data set. This necessitates gathering a set of dates or periods, as well as the prices of a specific currency or stock. As a result, the available, high, medium, and close prices of a selected currency or stock for specific dates must be obtained. When testing intraday trading strategies, only time-series data is required; however, data for multiple days is required when testing inter-day trading strategies.
By entering the stock's or currency pair's symbol, this information can be accessed via Altrady, Google Finance, or any other financial website that provides prices for different stocks and currencies. Importing and loading historical data into Excel is a time-consuming process. Yahoo Finance provides a direct download option to an Excel spreadsheet for the selected data. Some third-party applications may also be used to copy the data into an Excel sheet. After that, rename and save the file.
The next step is to remove any unnecessary columns and details. Certain traders may only require the open and close values and thus disregard the high, low, and volume values for the chosen timeframes. Furthermore, ensure that the data is presented chronologically from the earliest to the most recent date. In Excel, go to the Data>Sort menu and select the Sort option.
Once the data for backtesting is ready, you must select an indicator or the formula used to calculate the indicator, as well as define trading rules for the strategy under consideration. There are numerous Excel add-ons on the market that have a wide range of predefined metrics for backtesting.
Trading rules are then determined, such as entering a long position when an indicator approaches a certain threshold or exiting a short position when the technical indicator reaches a different level. At this stage, additional rules for remaining neutral or adopting variable positions are defined.
The next step is to start with a cash valuation of $10,000 or $15,000 and then add or subtract based on the short or long positions taken during the testing process. The daily returns are calculated automatically using the specified formula. The outcomes will then be graphed.
A Few Backtesting Strategy Tips
Historically, the first step in developing a backtesting strategy has been to acquire high-quality data. Without evidence from a high-quality order book, the findings would be somewhat erroneous. Make assumptions based on unreliable backtesting tools can be expensive in the long run. It can lead to us having irrational aspirations for a plan that depletes our portfolio.
Don't forget to simulate trading fees, slippage, and the bid-ask spread while developing a backtesting platform. Both of these facets of a backtest may have a significant impact. Eliminating any one of these elements from the backtesting process will mean the difference between a viable and an unprofitable approach.
Finally, continue experimenting prior to deploying a technique focused on backtests. When you believe you have completed your research, repeat it. Rather than 100 examinations, conduct 100,000 tests. Backtesting is the most effective method for us to comprehend a strategy's actions. Consider developing new theories for tactics and evaluating them to detect new ones. Maintain this loop of exploration before you discover solutions that work for you.