![]() Trading Strategy is a protocol for algorithmic trading of crypto-assets in decentralized markets. Thanks to TimescaleDB, they can focus on solving business problems without a need to build the infrastructure layer. In this edition, Mikko Ohtamaa, CEO at Trading Strategy, joins us to share how they give traders and investors direct access to high-quality trading strategies and real-time control over their assets by integrating market data about thousands of crypto assets, algorithms, profitability simulation, and more into one solution. See Part 3 of this series: Moving Average Trading Strategies.This is an installment of our “Community Member Spotlight” series, where we invite TimescaleDB community members to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems. Finally, we will look into the issue of optimizing the strategy parameters and how this can improve our return to risk profile. At the same time we will start looking into the risk of the strategy and present appropriate metrics to measure it. In what follows, we will start designing a more complex strategy, the weights of which will not be constant over time. Risk is the most important consideration in any investment strategy and is closely related to the expected returns. Ideally, we would like weights that change over time so that we can take advantage of price swings and other market events.Īlso, we have said nothing at all about the risk of this strategy. Furthermore, the weights here are constant over time. This will not affect the strategy we presented as the returns on the days the markets are closed are 0, but it may potentially affect other types of strategies. ![]() Let us not forget that we have used ALL weekdays in our example, but we do know that on some days the markets are not trading. However, the simulation is not completely accurate. Although simple, the strategy does produce a healthy $8.85\%$ per year. The investor simply splits up the available funds in the three assets and keeps the same position throughout the period under investigation. Our strategy is a very simple example of a buy-and-hold strategy. ![]() Of course, if $W<0$, our net position is short, which means we are currently holding more than $N$ dollars which is the initial value of the portfolio. \begin^K w_i\left(t\right) < 1$, then it means that our portfolio includes $\left(1-W\right)N$ dollars in cash. The most frequently used forms used are relative returns defined as These time-series can and do assume negative values and also, their statistical properties are usually more stable than the ones of price time-series. In addition, price time-series are usually non-stationary, that is their statistical properties are less stable over time.Īn alternative approach is to use time-series which correspond not to actual values but changes in the monetary value of the asset. Prices are usually only positive, which makes it harder to use models and approaches which require or produce negative numbers. However, price time-series have some drawbacks. Working with this type of time-series can be more intuitive as people are used to thinking in terms of prices. bonds, one could be using a time-series expressing the price of the bond as a percentage of a given reference value, in this case the par value of the bond. Similarly, if working with fixed income instruments, e.g. ![]() One approach would be to use the price time-series directly and work with numbers that correspond to some monetary value.įor example, a researcher could be working with time-series expressing the price of a given stock, like the time-series we used in the previous article. There are several ways one can go about when a trading strategy is to be developed.
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