Strategies for algorithmic trading, Navigation menu


Mathematical Finance What about forming your own quantitative binary options with a deposit of 500 This generally requires but is not limited to expertise in one or more of the following categories: Market microstructure - For higher frequency strategies in particular, one can make use of market microstructure, i.

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Different markets will have various technology limitations, regulations, market participants and constraints that are all open to exploitation via specific strategies. This is a very sophisticated area and retail practitioners will find it hard to be competitive in this space, particularly as the competition includes large, well-capitalised quantitative hedge funds strategies for algorithmic trading strong technological capabilities. Fund structure - Pooled investment funds, such as pension funds, private investment partnerships hedge fundscommodity trading advisors and mutual funds strategies for algorithmic trading constrained both by heavy regulation and their large capital reserves.

Thus certain consistent behaviours can be exploited with those who are more nimble. For instance, large funds are subject to capacity constraints due to their size. Thus if they need to rapidly offload sell a quantity of securities, they will have to stagger it in order to avoid "moving the market". Sophisticated algorithms can take advantage of this, and other idiosyncrasies, in a general process known as fund structure arbitrage.

Classifiers such as Naive-Bayes, et al. If you have a background in this area you may have some insight into how particular algorithms might be applied to certain markets. There are, of course, many other areas for quants to investigate. We'll discuss how to come up with custom strategies in detail in a later article.

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By continuing to monitor these sources on a weekly, or even daily, basis you are setting yourself up to receive a consistent list of strategies from a diverse range of sources.

The next step is to determine how to reject a large subset of these strategies in order to minimise wasting your time and backtesting resources on strategies that are likely to be unprofitable. Evaluating Trading Strategies Strategies for algorithmic trading first, and arguably most obvious consideration is whether you actually understand the strategy. Would you be able to explain the strategy concisely or does it require a string of caveats and endless parameter lists? In addition, does the strategy have a good, solid basis in reality?

For instance, could you point to some behavioural strategies for algorithmic trading or fund structure constraint that might be causing the pattern s you are attempting to exploit? Would this constraint hold up to a regime change, such as a dramatic regulatory environment disruption?

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Does strategies for algorithmic trading strategy rely on complex statistical or mathematical rules? Does it apply to any financial time series or is it specific to the asset class that it is claimed to be profitable on? You should constantly be thinking about these factors when evaluating new trading methods, otherwise you may waste a significant amount of time attempting to backtest and optimise unprofitable strategies.

Once you have determined that you understand the basic principles of the strategy you need to decide whether it fits with your aforementioned personality profile. This is not as vague a consideration as it sounds! Strategies will differ substantially in their performance characteristics. There are certain personality types that can handle more significant periods of drawdown, or are willing to accept greater risk for larger return.

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Despite the fact that we, as quants, try and eliminate as much cognitive bias as possible and should be able to evaluate a strategy dispassionately, biases will always creep strategies for algorithmic trading.

Thus we need a consistent, unemotional means through which strategies for algorithmic trading assess the performance of strategies. Here is the list of criteria that I judge a potential new strategy by: Methodology - Is the strategy momentum based, mean-reverting, market-neutral, directional? Does the strategy rely on sophisticated or complex! Do these techniques introduce a significant quantity of parameters, which might lead to optimisation bias?

Is the strategy likely to withstand a regime change i. It quantifies how much return you can achieve for the level of volatility endured by the equity curve. Naturally, we need to determine the period and frequency that these returns and volatility i. A higher frequency strategy will require greater sampling rate of standard deviation, but a shorter overall time period of measurement, for instance. Leverage - Does the strategy require significant leverage in order to be profitable?

Does the strategy necessitate the use of leveraged derivatives contracts futures, options, swaps in order to make a return?

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These leveraged contracts can have heavy volatility characterises and thus can easily lead to margin calls. Do you have the trading capital and the temperament for such volatility?

Frequency - The frequency of the strategy is intimately linked to your technology stack and thus technological expertisethe Sharpe ratio and overall level of transaction costs.

All other issues considered, higher frequency strategies require more capital, are more sophisticated and harder to implement.

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However, assuming your backtesting engine is sophisticated and bug-free, they will often have far higher Sharpe ratios. Volatility - Volatility is related strongly to the "risk" of the strategy. The Sharpe ratio characterises this. Higher volatility of the underlying asset classes, if unhedged, often leads to higher volatility in the strategies for algorithmic trading curve and thus smaller Sharpe ratios. I am of course assuming that the positive volatility is approximately equal to the negative volatility.

Some strategies may have greater downside volatility. You need to be aware of these attributes. One can have a very profitable strategy, even if the number of losing trades exceed the number of winning trades. Momentum strategies tend to have this pattern as they rely on a small number of "big hits" in order to be profitable. Mean-reversion strategies tend to have opposing profiles where more of the trades are "winners", but strategies for algorithmic trading losing trades can be quite severe.

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Maximum Drawdown - The maximum drawdown is the strategies for algorithmic trading overall peak-to-trough percentage drop on the equity curve of the strategy. Momentum strategies are well known to suffer from periods of extended drawdowns due to a string of many incremental losing trades. Many traders will give up in periods of extended drawdown, even if historical testing has suggested this is "business as usual" for the strategy. You will need to determine what percentage of drawdown and over what time period you can accept before you cease trading your strategy.

This is a highly personal decision and thus must be considered carefully. Capacity determines the scalability of the strategy to further capital. Many of the larger hedge funds suffer from significant capacity problems as their strategies increase in capital allocation. Parameters - Certain strategies especially those found in the machine learning community require a large quantity of parameters.

Fortunately, with significant advances in technology, algorithmic trading strategies are now accessible for all types of traders across nearly all major markets and is just one reason this form of trading is becoming increasingly popular.

Every extra parameter that a strategy requires leaves it more vulnerable to optimisation bias also known as "curve-fitting". You should try and target strategies with as few parameters as possible or make sure you have sufficient quantities of data with which to test your strategies on.

Benchmark - Nearly all strategies unless characterised as "absolute return" are measured against some performance benchmark. The benchmark is usually an index that characterises a large sample of the underlying asset class that the strategy trades in.

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You will hear the terms "alpha" and "beta", applied to strategies of this type. We will discuss these coefficients in depth in later articles.

Notice that we have not discussed the actual returns of the strategy. Why is this?

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In isolation, the returns actually provide us with limited information as to the effectiveness of the strategy. They don't give you an insight into leverage, volatility, benchmarks or capital requirements. Thus strategies are rarely judged on their returns alone.

Always consider the risk attributes of a strategy before looking at the returns. At this stage many of the strategies found from your pipeline will be rejected out of hand, since they won't meet your capital requirements, leverage constraints, maximum drawdown tolerance or volatility preferences. The strategies that do remain can now be considered for backtesting.

However, before this is possible, it is necessary to consider one final rejection criteria - that of available historical data on which to test these strategies. Obtaining Historical Data Nowadays, the breadth of the technical requirements across asset classes for historical data storage is substantial.

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In order to remain competitive, both the buy-side funds and sell-side investment banks invest heavily in their technical infrastructure. It is imperative to consider its importance. In particular, we are interested in timeliness, accuracy and storage requirements. I will now outline the basics of obtaining historical data and how to store it.

Unfortunately this is a very deep and technical topic, so I won't be able to say everything in this article. However, I will be writing a lot more about this in the future as my prior industry experience in the financial industry was chiefly concerned with financial data acquisition, storage and access. In the previous section we had set up a strategy pipeline that allowed us to reject certain strategies based on our own personal rejection criteria. In this section we will filter more strategies based on our own preferences for obtaining historical data.

The chief considerations especially at retail practitioner level are the costs of the data, the storage requirements and your level of technical expertise.

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We also need to discuss the different types of available data and the different considerations that each type of data will impose on us. Let's begin by discussing the types of data available and the key issues we will need to think about: Fundamental Data - This includes data about macroeconomic trends, such as interest rates, inflation figures, corporate actions dividends, stock-splitsSEC filings, corporate accounts, earnings figures, crop reports, meteorological data etc.

This data is often used to value companies or other assets on a fundamental basis, i.

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It does not include stock price series. Some fundamental data is freely available from government websites. Other long-term historical fundamental data can be extremely expensive.

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Storage requirements are often not particularly large, unless thousands of companies are being studied at once. News Data - News data is often qualitative strategies for algorithmic trading nature. It consists of articles, blog posts, microblog posts "tweets" and editorial. Machine learning techniques such as classifiers are often used to interpret sentiment. This data is also often freely available or cheap, via subscription to media outlets.

The newer "NoSQL" document storage databases are designed to store this type of unstructured, qualitative data. Asset Price Data - This is the options bonus data domain of the quant. It consists of time series of asset prices. Equities stocksfixed income products bondscommodities and foreign strategies for algorithmic trading prices all sit within this class.

Daily historical data is often straightforward to obtain for the simpler asset classes, such as equities. However, once accuracy and cleanliness are included and statistical biases removed, the data can become strategies for algorithmic trading.