Our R&D team developing tools for asset optimization and management, cooperating with Mendel University in Brno has launched a new trading system based on long-term effects of contango decay and volatility lowering. The system composes synthetic short position via option contracts for underlying asset ETF UVXY. An alfa factor is formed with regular technical indicators calculated from five-minute bars.
The most difficult task in backtesting script development was to manage situation where the script takes values based on five-minute bars calculation but the trading trigger may occur at anytime (not at time five-minute bar closes only). The solution depends on dynamic bar creation which gradually collects the last bar data (every minute). The scripts looks at this bar as five-minute bar.
After the firts test, we made (consistent with our principles) optimization via parameter sweep. The fitting rapidly improved the performance and stability of the system (out-of-sample). Due to esotheric assets utilitization (as the options are) we proceed to the next step of our test – the paper trading. We have chosen the paper trading rather early because of option strategies difficulties. We have to face missing option data with interpolation and extrapolation of volatility surface with appropriate model. Simply said we will calculate the missing data but it is slightly time demanding.
All in all the next trading system was launched besides Momentum Formula, indeed temporary for testing regime only. Launch more than one trading system provide us valuable information for portfolio optimization.
We are going to report the performance and next development of the system through next posts.
Michal Dufek, Head of Financial Research Software Development
On July 31, 2019, a meeting of representatives of companies and universities took place in Bielefeld, Germany, on the premises of the Chamber of Commerce of East Westphalia.
The whole event was organized in the format of Barcamp, ie a conference with a free program focusing on current topics in business and digitization.
More than 70 participants from Germany, the Czech Republic, Slovakia and Estonia discussed topics such as investing in the digital era, the use of blockchain as a tool for transparent money transfers, data protection or smart cities.
The aim of the organizers was to combine students’ enthusiasm for new technologies and the experience of the representatives of large companies, which led to interesting discussions and brainstorming on future developments in digital services.
CYRRUS ADVISORY FRS (Financial Research Software) team took part in the discussion in the field of finding statistically relevant market situations, the alpha of investment strategies.
Overseeing the asset management is being taken over by trading systems based on data analysis, artificial intelligence, machine-learning methods, predominantly the neural networks. Robotic trading based on algorithms will outperform man. Do you have the feeling you have already read this somewhere? Most likely yes, recently, there have been a lot of marketing materials and academic publications issued, focusing on modern technologies enforcement into financial assets management.
The articles have a very similar structure – initially, they list “breaking-through” discoveries in the field of information technologies and data analysis, not forgetting to name magical words such as “artificial intelligence”, “data analysis”, “neural networks” etc. The readers are further introduced to the advantages these technologies bring in comparison to the financial assets management by a man of flesh and bones. These articles and publication materials are very similar in trying to get out the most of the upsurge of modern technologies. Thereafter, in most cases, there is information about the author’s individual solution or service and, there, the magical words start disappearing or they appear vaguely and on a general level. When such promoted services or solutions are investigated into greater detail, more or less the same approaches which can be handled by a better-developed spreadsheet editor are seen. The message could be conveyed roughly like this: “There are brilliant tools which are gaining ground in the asset management, we deal with them… but eventually, we still do the techniques which can be managed by a “better excel” because it is just enough for us.” It is a practical example of what is nowadays called buzzword. Most of the current solutions offered in the market say: “We need to follow the technological development and we are unique in this owing to our approach… as a result, we fill our portfolio with shares of excellent technological companies (e.g. A, B, C) and the following year we exchange them for X, Y, Z.” The point is that from the global view, these changes are only cosmetic and the high dependency on the global market development remains.
Yes, there are companies that really work on this basis and use a “state-of-art” approach which they successfully apply in practice. The pace is set by companies such as the US Renaissance or Two Sigma. They, however, never spread these words and what they show are real results. Why would we be using the top-notch technologies if they did not deliver an added value in the form of better results? This mind-set is so intuitive and straightforward that it is partly in contradiction with our rooted habits – for example, the dependency of strategy profits on the global stock market development. If a trading strategy is correlated with the activity of underlying assets or with stock index S&P500, it is not good and this strategy must be immediately adjusted or is completely removed from further development – this fact is researched in the first set of tests in the new trading system which is being developed.
The point of such an approach is an effort to maintain high stability, not higher profitability. Mathematically speaking, the optimisation problem is not maximising the profit but minimising the risk. The pursuit of maximal profit is the subject of many statistical delusions and mathematical paradoxes that lurk at each of research steps from the historical bias, overfitting, high degree of correlation with the global financial market up to the uncontrollable degree of risk. In the moment of reaching risk control, the trading system gains a truly powerful tool in the form of stability. Therefore, profit is “only” the function of available capital and willingness to undertake risks.
The basic optimisation condition is the starting point for a long journey of research and development which results in a viable and autonomous trading system.
- The first building block of a trading system is the identification of a trading opportunity. A considerable part of investors I met lives in the belief that “the secret” is just this, however, the truth is that it is only the first step to create a stable and profitable system. Trading opportunities can be of many forms, from “getting” the difference between offer and demand, technical analysis which is virtually nothing but looking for systematic deviation in the price development up to fundamental analysis which seeks trading opportunities based on the development of determinants (for example the weather at crops and gas, political situation and reserves at oil, indebtedness at companies etc.). This trading opportunity is represented by a set of rules, for example for opening or closing a trade. The power or, if you want, the size of such a trading opportunity is measured by the so-called alpha. The moment when your trading rules generate positive alpha, you made the first step leading to the creation of a profitable trading system.
- The next step involves brute force computing. There is often another mistake (in this case made by a little bit more advanced creators of trading systems). One group prefers using “brute force computing” for seeking trading opportunities without the first step (trading opportunity identification). This effort leads to the creation of a rule set that historically led to profits but the absence of trading and economic sense is the cause for very little information value on the system functioning in the future. This phenomenon is called overfitting and it is reported to have low stability of real performance. On the other hand, the other group denies the use of so-called “yield curve fitting” because of the above-mentioned reason and the rules are set based on the experience. By taking this step, the author can avoid the danger of overfitting, however, it is highly likely that they are located at a point outside the local maximum or, in other words, they do not use optimal rules for the particular trading opportunity by which they do not fit within the range of effective boundary of a set of possible solutions. Using this brute force computing for optimising parameters (using genetic programming or so-called parameter sweeping, less brute option are neural networks) in the paradigm of the particular trading opportunity moves the trading system to the local maximum of a set of possible solutions.
- In the third step, the trading system is placed into a larger context in a way so that outputs (realised trades) are compared with the development of potential determinants or predictors (among these determinants can be for example volatility, time premium of futures contracts, risk premium of option chains but also statistical quantities such as skewness, sharpness, average, Hurst exponent measuring the persistence of trend etc.). After identifying statistically significant determinants, there will be created a scoring model that evaluates the dependency of the trading system success on the development of significant determinants.
- The output of the model is information gaining binary values (imagine a traffic light on a pedestrian crossing) that tells the trading system when it is the most convenient time to trade and when it is better to hold back (when there is a higher likelihood to succeed).
- In case of having more trading systems of this kind, the trader can proceed to the last step in which the involvement of individual trading systems is optimised in a way so that the trader can be in the range of effective boundary of the portfolio or so that the maximal yield is gained at the required level of risk.
In the end, a person enters the whole process as a checker and “maintenance man” of such a trading system. No strategy can run in the real environment without maintenance and calibration. This role is still held by man as well as the role of a checker. State-of-art technologies work as a good technical tool for achieving all goals – these technologies are faster, more accurate, more computing-efficient but they can never solve everything for man, they cannot define a problem and they cannot solve a wrongly defined problem.
Michal Dufek, Head of Financial Research Software Development
Test run of our automatic oil and gold based Momentum Formula trading system has been running since February. This paper presents the results of the system.
|Number of test days||138 (2/7/2019)|
|Number of trades||74|
|Value of capital 15/2/2019||1 000 USD|
|Value of capital 2/7/2019||1 158 USD|
|Profit (YTD)||15.8 %|
|Risk (Maximum Drop)||11.42 %|
|Profit per annum||47 %|
|SharpeRatio (number of profit units per risk unit)||2.18|
Orders are executed through Interactive Brokers.
The results are displayed including errors that have occurred during the testing period (for example, the system crashed over the weekend).
We have further improved the system during the reporting period. We’ve eliminated infrastructure bugs, and we’ve enriched the model with new information to eliminate sinks. Below you can see the slumps achieved by the reported strategy (blue curve) and the slump in the improved version that we will soon deploy (orange curve).
The risk level of this system in the case of oil is about 5% (the strategy had the largest value of the property dicrease about 5%). The underlying asset (crude oil) for the reporting period (2015 – end 2018) shows a maximum fall of around 50% (decline episode June 2015 – February 2016, followed by September 2018 – December 2018).
Head of FRS Development, Michal Dufek
The article describes a software application from the area of Financial Research Software whose purpose is to look for trade opportunities in financial markets. The software is designed for analysts and portfolio managers, traders with securities, alternative fonds, Fond of Qualified Investors and banks asset management departments. The added value is not only the top-notch sophisticated tools for data analysis, statistics and likelihood but also the fact that the users are given a ready-made workflow of individual trading approaches where anyone can participate in using trade opportunities which are offered in the global financial market.
- What? To seek and use a trade opportunity.
- How? By searching the market according to a specific trading approach, followed by testing of the success on the historical time series.
- When? The results of the functionality are provided to you straight after using the application.
Trading systems behaviour simulation software
Today’s post will take a look at the creation of our team from a different angle. Our team is the developer of a software application (so-called Financial Research Software) which will be used for seeking trade opportunities and their testing. This application can be used by all analysts and portfolio managers, traders with securities, alternative fonds, Fond of Qualified Investors and banks asset management departments. The application does not bring only another tool for effective financial assets management – the main objective of the application is to bring users a workflow which will intuitively guide through the world of professional data analysis, statistics and also likelihood and trading with the aim to take the maximum advantage of trade opportunities which appear in global financial markets.
In the beginning, it is right to realise that looking for trade opportunities and building of trading systems is a very complex process which includes activities from various areas (so-called building blocks). For this reason, to identify and classify individual “building blocks”, we create two categories, horizontal and vertical scaling which can help us understand application operation.
The aim of horizontal scaling is to cluster trade opportunities according to trading approaches. A trading approach is a style (with a little exaggeration – a trade model) by which a trader is trying to reach profit. An example can be the relative values trading approach that can include different types of arbitrages, long/short strategies, pair-trading etc. What all these approaches have in common, is a fact that the trader looks for pairs of financial assets which have some “economical” bond between each other and watches mutual (relative – herby the name relative values) determination of these two assets. In the situation when there is a distortion between relative assessment, the trader finds a trade opportunity (so-called their market) and this situation is used by opening trading positions which benefit from gradual disappearance of this “error” in determination (above mentioned as distortion). This scheme illustrates situations which happened after the fall of Lehman Brothers. There was a situation in the market when, due to global tensions and increased volatility, the prices of CDS on Czech government bonds were higher them prices of CDS on bonds of a Czech company called ČEZ. Looking at this from the theory perspective which says that “risk of one company” = “risk of the country in which the company trades” + “specific risk of the industry/particular company”, this situation proves to be nonsensical. At the moment when more such stress situations appear, also more trade opportunities appear. For the user, the motivation of the relative values module is to find these distortions in the real-time and use trade opportunities which these situations offer. Notice that this schematic description of revealing the process and using the trade opportunity does not include anything about “prediction of the future value”. Traders often concentrate too much on “prediction and forecasting” of the future values, however, going through this extremely difficult discipline is often not necessary, as the mentioned example shows.
The application is constructed in such a way that 1 trading approach = 1 module. Trading approaches used in this application are:
Besides trade opportunities, there is a multitude of other areas (building blocks) which contribute to revealing trade opportunities, creating trading systems or increasing their efficiency. Vertical scaling divides a trading system into building blocks which have a specific role within it. These blocks can be in the intermediate step clustered into areas which technically contribute to the creation and management of trading strategies (trading, portfolio optimisation, statistics and likelihood). The purpose of vertical scaling is to illustrate the continuity of work (= continuity of applications of individual methodical bundles/blocks) when creating a trading system. Blocks which are considered in the application are:
|models describing a particular time series||Statistics characterising dependence behaviour of price and achieved results|
|persistence of time series development|
|approximated time series entropy|
|continuous optimisation of financial assets included in the portfolio to minimise risk/maximize return||Portfolio optimisation|
The paragraphs above show that our team is not focusing only on trading even though it dedicates its efforts on topics close to trading. Execution, position management and output rules are of course an inseparable part of our job but it is actually only one of the areas we deal with. Areas our team deals with are closely connected with creating an application which includes modules for testing and building trading blocks where 1 module = 1 trading approach. Each trading approach can, of course, generate a lot of trading systems (strategies).
By all of this, users (analysts/portfolio managers) are given a robust tool which can help find and use trade opportunities until their termination in order to find a new opportunity as soon as possible. As the saying goes: „strategies die, skills survive“.
Research & Development team is currently focused on development of trading strategies applied to Crude Oil WTI, Natural Gas and Gold (futures contracts). These commodities are traded on centralized exchanges NYMEX and COMEX which are located in USA and each day trading activity exceeds billions of dollars. Very high liquidity and interest of other market subjects create a suitable environment for short-term momentum trading strategies. It is appropriate to include these in the research portfolio due to the necessary volatility and the structure of the financial leveraged instrument itself.
R&D team develops and optimizes trading strategies on rule-based concepts that has positive performance and can predict upcoming short-term price momentum with sufficient probability.
Implementation of new technologies can help R&D rigorously analyze the performance of trading strategies and related risks with highest exact level. Against the “old” technologies, the modern ones can implement real-time execution of trading orders based on all realized trades in the selected time period. Application of old technologies for trading strategy performance analysis often lead to unreliable statistical results and trading strategy with great historical performance often fail in the real market.
Development and optimization with modern technologies is the one of the main goals of the project. When trading strategies for Crude Oil WTI, Natural Gas and Gold are done they will be included in metastrategy-driven portfolio with smart asset allocation.
- Crude Oil WTI, Natural Gas, Gold futures contracts
- Implementation using modern frameworks and libraries: Zipline, Pyfolio, Pandas, MetaTrader, NumPy
- Short-term price momentum trading strategies development
- Our innovation against existing trading strategies is based on implementation of modern frameworks and analytical tools
- Our research aims to implement proposed trading strategies to the real market in a short period. All strategies will be included in a metastrategy-driven portfolio